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"execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "import paddle\n", + "import paddle.nn as nn\n", + "import paddle.nn.functional as F\n", + "import parl\n", + "\n", + "class CartpoleModel(parl.Model):\n", + " \"\"\" Linear network to solve Cartpole problem.\n", + " Args:\n", + " n_states (int): Dimension of observation space.\n", + " n_actions (int): Dimension of action space.\n", + " \"\"\"\n", + "\n", + " def __init__(self, n_states, n_actions):\n", + " super(CartpoleModel, self).__init__()\n", + " hid1_size = 128\n", + " hid2_size = 128\n", + " self.fc1 = nn.Linear(n_states, hid1_size)\n", + " self.fc2 = nn.Linear(hid1_size, hid2_size)\n", + " self.fc3 = nn.Linear(hid2_size, n_actions)\n", + "\n", + " def forward(self, obs):\n", + " h1 = F.relu(self.fc1(obs))\n", + " h2 = F.relu(self.fc2(h1))\n", + " Q = self.fc3(h2)\n", + " return Q" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "import parl\n", + "import paddle\n", + "import numpy as np\n", + "\n", + "\n", + "class CartpoleAgent(parl.Agent):\n", + " \"\"\"Agent of Cartpole env.\n", + " Args:\n", + " algorithm(parl.Algorithm): algorithm used to solve the problem.\n", + " \"\"\"\n", + "\n", + " def __init__(self, algorithm, n_actions, e_greed=0.1, e_greed_decrement=0):\n", + " super(CartpoleAgent, self).__init__(algorithm)\n", + " assert isinstance(n_actions, int)\n", + " self.n_actions = n_actions\n", + "\n", + " self.global_step = 0\n", + " self.update_target_steps = 200\n", + "\n", + " self.e_greed = e_greed\n", + " self.e_greed_decrement = e_greed_decrement\n", + "\n", + " def sample(self, obs):\n", + " \"\"\"Sample an action `for exploration` when given an observation\n", + " Args:\n", + " obs(np.float32): shape of (n_states,)\n", + " Returns:\n", + " act(int): action\n", + " \"\"\"\n", + " sample = np.random.random()\n", + " if sample < self.e_greed:\n", + " act = np.random.randint(self.n_actions)\n", + " else:\n", + " if np.random.random() < 0.01:\n", + " act = np.random.randint(self.n_actions)\n", + " else:\n", + " act = self.predict(obs)\n", + " self.e_greed = max(0.01, self.e_greed - self.e_greed_decrement)\n", + " return act\n", + "\n", + " def predict(self, obs):\n", + " \"\"\"Predict an action when given an observation\n", + " Args:\n", + " obs(np.float32): shape of (n_states,)\n", + " Returns:\n", + " act(int): action\n", + " \"\"\"\n", + " obs = paddle.to_tensor(obs, dtype='float32')\n", + " pred_q = self.alg.predict(obs)\n", + " act = pred_q.argmax().numpy()[0]\n", + " return act\n", + "\n", + " def learn(self, obs, act, reward, next_obs, terminal):\n", + " \"\"\"Update model with an episode data\n", + " Args:\n", + " obs(np.float32): shape of (batch_size, n_states)\n", + " act(np.int32): shape of (batch_size)\n", + " reward(np.float32): shape of (batch_size)\n", + " next_obs(np.float32): shape of (batch_size, n_states)\n", + " terminal(np.float32): shape of (batch_size)\n", + " Returns:\n", + " loss(float)\n", + " \"\"\"\n", + " if self.global_step % self.update_target_steps == 0:\n", + " self.alg.sync_target()\n", + " self.global_step += 1\n", + "\n", + " act = np.expand_dims(act, axis=-1)\n", + " reward = np.expand_dims(reward, axis=-1)\n", + " terminal = np.expand_dims(terminal, axis=-1)\n", + "\n", + " obs = paddle.to_tensor(obs, dtype='float32')\n", + " act = paddle.to_tensor(act, dtype='int32')\n", + " reward = paddle.to_tensor(reward, dtype='float32')\n", + " next_obs = paddle.to_tensor(next_obs, dtype='float32')\n", + " terminal = paddle.to_tensor(terminal, dtype='float32')\n", + " loss = self.alg.learn(obs, act, reward, next_obs, terminal)\n", + " return loss.numpy()[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import gym\n", + "import numpy as np\n", + "import parl\n", + "\n", + "from parl.utils import logger, ReplayMemory\n", + "from parl.algorithms import DQN\n", + "\n", + "LEARN_FREQ = 5 # training frequency\n", + "MEMORY_SIZE = 200000\n", + "MEMORY_WARMUP_SIZE = 200\n", + "BATCH_SIZE = 64\n", + "LEARNING_RATE = 0.0005\n", + "GAMMA = 0.99\n", + "\n", + "# train an episode\n", + "def run_train_episode(agent, env, rpm):\n", + " total_reward = 0\n", + " obs = env.reset()\n", + " step = 0\n", + " while True:\n", + " step += 1\n", + " action = agent.sample(obs)\n", + " next_obs, reward, done, _ = env.step(action)\n", + " rpm.append(obs, action, reward, next_obs, done)\n", + "\n", + " # train model\n", + " if (len(rpm) > MEMORY_WARMUP_SIZE) and (step % LEARN_FREQ == 0):\n", + " # s,a,r,s',done\n", + " (batch_obs, batch_action, batch_reward, batch_next_obs,\n", + " batch_done) = rpm.sample_batch(BATCH_SIZE)\n", + " train_loss = agent.learn(batch_obs, batch_action, batch_reward,\n", + " batch_next_obs, batch_done)\n", + "\n", + " total_reward += reward\n", + " obs = next_obs\n", + " if done:\n", + " break\n", + " return total_reward\n", + "\n", + "\n", + "# evaluate 5 episodes\n", + "def run_evaluate_episodes(agent, env, eval_episodes=5, render=False):\n", + " eval_reward = []\n", + " for i in range(eval_episodes):\n", + " obs = env.reset()\n", + " episode_reward = 0\n", + " while True:\n", + " action = agent.predict(obs)\n", + " obs, reward, done, _ = env.step(action)\n", + " episode_reward += reward\n", + " if render:\n", + " env.render()\n", + " if done:\n", + " break\n", + " eval_reward.append(episode_reward)\n", + " return np.mean(eval_reward)\n", + "\n", + "\n", + "def main(args):\n", + " env = gym.make('CartPole-v0')\n", + " n_states = env.observation_space.shape[0]\n", + " n_actions = env.action_space.n\n", + " logger.info('n_states {}, n_actions {}'.format(n_states, n_actions))\n", + "\n", + " # set action_shape = 0 while in discrete control environment\n", + " rpm = ReplayMemory(MEMORY_SIZE, n_states, 0)\n", + "\n", + " # build an agent\n", + " model = CartpoleModel(n_states=n_states, n_actions=n_actions)\n", + " alg = DQN(model, gamma=GAMMA, lr=LEARNING_RATE)\n", + " agent = CartpoleAgent(\n", + " alg, n_actions=n_actions, e_greed=0.1, e_greed_decrement=1e-6)\n", + "\n", + " # warmup memory\n", + " while len(rpm) < MEMORY_WARMUP_SIZE:\n", + " run_train_episode(agent, env, rpm)\n", + "\n", + " max_episode = args.max_episode\n", + "\n", + " # start training\n", + " episode = 0\n", + " while episode < max_episode:\n", + " # train part\n", + " for i in range(50):\n", + " total_reward = run_train_episode(agent, env, rpm)\n", + " episode += 1\n", + "\n", + " # test part\n", + " eval_reward = run_evaluate_episodes(agent, env, render=False)\n", + " logger.info('episode:{} e_greed:{} Test reward:{}'.format(\n", + " episode, agent.e_greed, eval_reward))\n", + "\n", + " # save the parameters to ./model.ckpt\n", + " save_path = './model.ckpt'\n", + " agent.save(save_path)\n", + "\n", + " # save the model and parameters of policy network for inference\n", + " save_inference_path = './inference_model'\n", + " input_shapes = [[None, env.observation_space.shape[0]]]\n", + " input_dtypes = ['float32']\n", + " agent.save_inference_model(save_inference_path, input_shapes, input_dtypes)\n", + "\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m[08-01 21:48:19 MainThread @3996942455.py:64]\u001b[0m obs_dim 4, act_dim 2\n", + "\u001b[32m[08-01 21:48:19 MainThread @3996942455.py:92]\u001b[0m episode:50 e_greed:0.0988929999999989 Test reward:18.4\n", + "\u001b[32m[08-01 21:48:20 MainThread @3996942455.py:92]\u001b[0m episode:100 e_greed:0.09794799999999795 Test reward:9.6\n", + "\u001b[32m[08-01 21:48:20 MainThread @3996942455.py:92]\u001b[0m episode:150 e_greed:0.0973899999999974 Test reward:37.8\n", + "\u001b[32m[08-01 21:48:20 MainThread @3996942455.py:92]\u001b[0m episode:200 e_greed:0.09684299999999685 Test reward:8.8\n", + "\u001b[32m[08-01 21:48:20 MainThread @3996942455.py:92]\u001b[0m episode:250 e_greed:0.09635499999999636 Test reward:9.4\n", + "\u001b[32m[08-01 21:48:21 MainThread @3996942455.py:92]\u001b[0m episode:300 e_greed:0.09585299999999586 Test reward:9.2\n", + "\u001b[32m[08-01 21:48:21 MainThread @3996942455.py:92]\u001b[0m episode:350 e_greed:0.09535799999999536 Test reward:9.2\n", + "\u001b[32m[08-01 21:48:21 MainThread @3996942455.py:92]\u001b[0m episode:400 e_greed:0.09486399999999487 Test reward:10.0\n", + "\u001b[32m[08-01 21:48:21 MainThread @3996942455.py:92]\u001b[0m episode:450 e_greed:0.09435299999999436 Test reward:9.2\n", + "\u001b[32m[08-01 21:48:22 MainThread @3996942455.py:92]\u001b[0m episode:500 e_greed:0.09384899999999385 Test reward:9.4\n", + "\u001b[32m[08-01 21:48:22 MainThread @3996942455.py:92]\u001b[0m episode:550 e_greed:0.09302299999999303 Test reward:69.0\n", + "\u001b[32m[08-01 21:48:25 MainThread @3996942455.py:92]\u001b[0m episode:600 e_greed:0.08774199999998775 Test reward:141.2\n", + "\u001b[32m[08-01 21:48:30 MainThread @3996942455.py:92]\u001b[0m episode:650 e_greed:0.0791019999999791 Test reward:184.0\n", + "\u001b[32m[08-01 21:48:35 MainThread @3996942455.py:92]\u001b[0m episode:700 e_greed:0.07011299999997012 Test reward:182.0\n", + "\u001b[32m[08-01 21:48:40 MainThread @3996942455.py:92]\u001b[0m episode:750 e_greed:0.06089099999996089 Test reward:197.4\n", + "\u001b[32m[08-01 21:48:45 MainThread @3996942455.py:92]\u001b[0m episode:800 e_greed:0.05139199999995139 Test reward:183.4\n", + "\u001b[32m[08-01 21:48:50 MainThread @3996942455.py:92]\u001b[0m episode:850 e_greed:0.042255999999942256 Test reward:153.0\n", + "\u001b[32m[08-01 21:48:55 MainThread @3996942455.py:92]\u001b[0m episode:900 e_greed:0.033495999999933496 Test reward:192.6\n", + "\u001b[32m[08-01 21:49:00 MainThread @3996942455.py:92]\u001b[0m episode:950 e_greed:0.024318999999924318 Test reward:166.6\n", + "\u001b[32m[08-01 21:49:06 MainThread @3996942455.py:92]\u001b[0m episode:1000 e_greed:0.014873999999916176 Test reward:187.0\n" + ] + } + ], + "source": [ + "import argparse\n", + "parser = argparse.ArgumentParser()\n", + "parser.add_argument(\n", + " '--max_episode',\n", + " type=int,\n", + " default=1000,\n", + " help='stop condition: number of max episode')\n", + "args = parser.parse_args(args=[])\n", + "\n", + "main(args)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.7.12 ('rl_tutorials')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.12" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "4f613f1ab80ec98dc1b91d6e720de51301598a187317378e53e49b773c1123dd" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/projects/PARL/README.md b/projects/PARL/README.md new file mode 100644 index 0000000..a8ddbb7 --- /dev/null +++ b/projects/PARL/README.md @@ -0,0 +1,11 @@ +[PARL](https://github.com/PaddlePaddle/PARL)是一个高性能、灵活的强化学习框架，由百度AI Studio开发。 + +## 安装 + +1. 安装parl，参考[PARL Github](https://github.com/PaddlePaddle/PARL) +2. 安装paddlepaddle：```pip install paddlepaddle``` + +## 常见问题 + +```jupyter-client 7.3.1 requires pyzmq>=22.3, but you have pyzmq 18.1.1 which is incompatible.```: +```pip install -U pyzmq``` \ No newline at end of file diff --git a/projects/README.md b/projects/README.md index ee63dd4..2ad650f 100644 --- a/projects/README.md +++ b/projects/README.md @@ -1,49 +1,34 @@ ## 0、写在前面 -本项目用于学习RL基础算法，尽量做到: **注释详细**(经过很长时间的纠结，还是中文注释好了！！！)，**结构清晰**。 +本项目用于学习RL基础算法，主要面向对象为RL初学者、需要结合RL的非专业学习者，尽量做到: **(中文)注释详细**，**结构清晰**。 -代码结构主要分为以下几个脚本： +注意本项目为实战内容，建议首先掌握相关算法的一些理论基础，再来享用本项目，理论教程参考本人参与编写的[蘑菇书](https://github.com/datawhalechina/easy-rl)。 +未来开发计划包括但不限于：多智能体算法、强化学习Python包以及强化学习图形化编程平台等等。 + +## 1、项目说明 + +项目内容主要包含以下几个部分： +* [Jupyter Notebook](./notebooks/)：使用Notebook写的算法，有比较详细的实战引导，推荐新手食用 +* [codes](./assets/)：这些是基于Python脚本写的算法，风格比较接近实际项目的写法，推荐有一定代码基础的人阅读，下面会说明其具体的一些架构 +* [parl](./PARL/)：应业务需求，写了一些基于百度飞浆平台和```parl```模块的RL实例 +* [附件](./assets/)：目前包含强化学习各算法的中文伪代码 + + +[codes](./assets/)结构主要分为以下几个脚本： * ```[algorithm_name].py```：即保存算法的脚本，例如```dqn.py```，每种算法都会有一定的基础模块，例如```Replay Buffer```、```MLP```(多层感知机)等等； * ```task.py```: 即保存任务的脚本，基本包括基于```argparse```模块的参数，训练以及测试函数等等； * ```utils.py```：该脚本用于保存诸如存储结果以及画图的软件，在实际项目或研究中，推荐大家使用```Tensorboard```来保存结果，然后使用诸如```matplotlib```以及```seabron```来进一步画图。 -## 运行环境 +## 2、运行环境 python 3.7、pytorch 1.6.0-1.9.0、gym 0.21.0 -或者在```README.md```目录下执行以下命令复现环境： +在项目根目录下执行以下命令复现环境： ```bash -conda env create -f environment.yaml +pip install -r requirements.txt ``` -## 使用说明 +## 3、使用说明 直接运行带有```train```的py文件或ipynb文件会进行训练默认的任务； 也可以运行带有```task```的py文件训练不同的任务 - -## 内容导航 - -| 算法名称 | 相关论文材料 | 环境 | 备注 | -| :--------------------------------------: | :----------------------------------------------------------: | ----------------------------------------- | :--------------------------------: | -| [On-Policy First-Visit MC](./MonteCarlo) | [medium blog](https://medium.com/analytics-vidhya/monte-carlo-methods-in-reinforcement-learning-part-1-on-policy-methods-1f004d59686a) | [Racetrack](./envs/racetrack_env.md) | | -| [Q-Learning](./QLearning) | [towardsdatascience blog](https://towardsdatascience.com/simple-reinforcement-learning-q-learning-fcddc4b6fe56),[q learning paper](https://ieeexplore.ieee.org/document/8836506) | [CliffWalking-v0](./envs/gym_info.md) | | -| [Sarsa](./Sarsa) | [geeksforgeeks blog](https://www.geeksforgeeks.org/sarsa-reinforcement-learning/) | [Racetrack](./envs/racetrack_env.md) | | -| [DQN](./DQN) | [DQN Paper](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf),[Nature DQN Paper](https://www.nature.com/articles/nature14236) | [CartPole-v0](./envs/gym_info.md) | | -| [DQN-cnn](./DQN_cnn) | [DQN Paper](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf) | [CartPole-v0](./envs/gym_info.md) | 与DQN相比使用了CNN而不是全链接网络 | -| [DoubleDQN](./DoubleDQN) | [DoubleDQN Paper](https://arxiv.org/abs/1509.06461) | [CartPole-v0](./envs/gym_info.md) | | -| [Hierarchical DQN](HierarchicalDQN) | [H-DQN Paper](https://arxiv.org/abs/1604.06057) | [CartPole-v0](./envs/gym_info.md) | | -| [PolicyGradient](./PolicyGradient) | [Lil'log](https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html) | [CartPole-v0](./envs/gym_info.md) | | -| [A2C](./A2C) | [A3C Paper](https://arxiv.org/abs/1602.01783) | [CartPole-v0](./envs/gym_info.md) | | -| [SAC](./SoftActorCritic) | [SAC Paper](https://arxiv.org/abs/1801.01290) | [Pendulum-v0](./envs/gym_info.md) | | -| [PPO](./PPO) | [PPO paper](https://arxiv.org/abs/1707.06347) | [CartPole-v0](./envs/gym_info.md) | | -| [DDPG](./DDPG) | [DDPG Paper](https://arxiv.org/abs/1509.02971) | [Pendulum-v0](./envs/gym_info.md) | | -| [TD3](./TD3) | [TD3 Paper](https://arxiv.org/abs/1802.09477) | [HalfCheetah-v2]((./envs/mujoco_info.md)) | | - - -## Refs - -[RL-Adventure-2](https://github.com/higgsfield/RL-Adventure-2) - -[RL-Adventure](https://github.com/higgsfield/RL-Adventure) - -[Google 开源项目风格指南——中文版](https://zh-google-styleguide.readthedocs.io/en/latest/google-python-styleguide/python_style_rules/#comments) \ No newline at end of file diff --git a/projects/assets/pseudocodes/pseudocodes.aux b/projects/assets/pseudocodes/pseudocodes.aux new file mode 100644 index 0000000..1373a7a --- /dev/null +++ b/projects/assets/pseudocodes/pseudocodes.aux @@ -0,0 +1,4 @@ +\relax +\@writefile{loa}{\contentsline {algorithm}{\numberline {}{\ignorespaces }}{1}{}\protected@file@percent } +\@writefile{loa}{\contentsline {algorithm}{\numberline {}{\ignorespaces }}{2}{}\protected@file@percent } +\gdef \@abspage@last{2} diff --git a/projects/assets/pseudocodes/pseudocodes.log b/projects/assets/pseudocodes/pseudocodes.log new file mode 100644 index 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zCn+C~=Y%Cpi>R)Y!gBtV4A2+E$8TXjk`Ti3FBuO=I*9-?KN!s7AN@z%&T3gE0%|H~}Q*ma=ad8jx~hIB(P4$l7o DQU;ol literal 0 HcmV?d00001 diff --git a/projects/assets/pseudocodes/pseudocodes.tex b/projects/assets/pseudocodes/pseudocodes.tex new file mode 100644 index 0000000..4db2296 --- /dev/null +++ b/projects/assets/pseudocodes/pseudocodes.tex @@ -0,0 +1,63 @@ +\documentclass[11pt]{ctexart} +\usepackage{ctex} +\usepackage{algorithm} +\usepackage{algorithmic} +\usepackage{amssymb} +\usepackage{amsmath} + + +\begin{document} + +\begin{algorithm} + \floatname{algorithm}{{DQN算法}} + \renewcommand{\thealgorithm}{} % 去掉算法标号 + \caption{} + \renewcommand{\algorithmicrequire}{\textbf{输入:}} + \renewcommand{\algorithmicensure}{\textbf{输出:}} + \begin{algorithmic} + % \REQUIRE $n \geq 0 \vee x \neq 0$ % 输入 + % \ENSURE $y = x^n$ % 输出 + \STATE 初始化策略网络参数$\theta$ % 初始化 + \STATE 复制参数到目标网络$\hat{Q} \leftarrow Q$ + \STATE 初始化经验回放$D$ + \FOR {回合数 = $1,M$} + \STATE 重置环境，获得初始状态$s_t$ + \FOR {时步 = $1,t$} + \STATE 根据$\varepsilon-greedy$策略采样动作$a_t$ + \STATE 环境根据$a_t$反馈奖励$s_t$和下一个状态$s_{t+1}$ + \STATE 存储transition即$(s_t,a_t,r_t,s_{t+1})$到经验回放$D$中 + \STATE 更新环境状态$s_{t+1} \leftarrow s_t$ + \STATE {\bfseries 更新策略：} + \STATE 从$D$中采样一个batch的transition + \STATE 计算实际的$Q$值，即$y_{j}= \begin{cases}r_{j} & \text {对于终止状态} s_{j+1} \\ r_{j}+\gamma \max _{a^{\prime}} Q\left(s_{j+1}, a^{\prime} ; \theta\right) & \text {对于非终止状态} s_{j+1}\end{cases}$ + \STATE 对损失 $\left(y_{j}-Q\left(s_{j}, a_{j} ; \theta\right)\right)^{2}$关于参数$\theta$做随机梯度下降 + \STATE 每$C$步复制参数$\hat{Q} \leftarrow Q$ + \ENDFOR + \ENDFOR + \end{algorithmic} +\end{algorithm} + +\clearpage + +\begin{algorithm} + \floatname{algorithm}{{SoftQ算法}} + \renewcommand{\thealgorithm}{} % 去掉算法标号 + \caption{} + \begin{algorithmic} + \STATE 初始化参数$\theta$和$\phi$% 初始化 + \STATE 复制参数$\bar{\theta} \leftarrow \theta, \bar{\phi} \leftarrow \phi$ + \STATE 初始化经验回放$D$ + \FOR {回合数 = $1,M$} + \FOR {时步 = $1,t$} + \STATE 根据$a_{t} \leftarrow f^{\phi}\left(\xi ; \mathbf{s}_{t}\right)$采样动作，其中$\xi \sim \mathcal{N}(\mathbf{0}, \boldsymbol{I})$ + \STATE 环境根据$a_t$反馈奖励$s_t$和下一个状态$s_{t+1}$ + \STATE 存储transition即$(s_t,a_t,r_t,s_{t+1})$到经验回放$D$中 + \STATE 更新环境状态$s_{t+1} \leftarrow s_t$ + \STATE 待完善 + \ENDFOR + \ENDFOR + + \end{algorithmic} +\end{algorithm} + +\end{document} \ No newline at end of file diff --git a/projects/codes/DQN/dqn.py b/projects/codes/DQN/dqn.py index 5ce5e1e..2b28757 100644 --- a/projects/codes/DQN/dqn.py +++ b/projects/codes/DQN/dqn.py @@ -5,7 +5,7 @@ @Email: johnjim0816@gmail.com @Date: 2020-06-12 00:50:49 @LastEditor: John -LastEditTime: 2022-07-20 23:57:16 +LastEditTime: 2022-08-11 09:52:23 @Discription: @Environment: python 3.7.7 ''' @@ -14,77 +14,39 @@ LastEditTime: 2022-07-20 23:57:16 import torch import torch.nn as nn -import torch.nn.functional as F import torch.optim as optim import random import math import numpy as np -class MLP(nn.Module): - def __init__(self, n_states,n_actions,hidden_dim=128): - """ 初始化q网络，为全连接网络 - n_states: 输入的特征数即环境的状态维度 - n_actions: 输出的动作维度 - """ - super(MLP, self).__init__() - self.fc1 = nn.Linear(n_states, hidden_dim) # 输入层 - self.fc2 = nn.Linear(hidden_dim,hidden_dim) # 隐藏层 - self.fc3 = nn.Linear(hidden_dim, n_actions) # 输出层 - - def forward(self, x): - # 各层对应的激活函数 - x = F.relu(self.fc1(x)) - x = F.relu(self.fc2(x)) - return self.fc3(x) - -class ReplayBuffer: - def __init__(self, capacity): - self.capacity = capacity # 经验回放的容量 - self.buffer = [] # 缓冲区 - self.position = 0 - - def push(self, state, action, reward, next_state, done): - ''' 缓冲区是一个队列，容量超出时去掉开始存入的转移(transition) - ''' - if len(self.buffer) < self.capacity: - self.buffer.append(None) - self.buffer[self.position] = (state, action, reward, next_state, done) - self.position = (self.position + 1) % self.capacity - - def sample(self, batch_size): - batch = random.sample(self.buffer, batch_size) # 随机采出小批量转移 - state, action, reward, next_state, done = zip(*batch) # 解压成状态，动作等 - return state, action, reward, next_state, done - - def __len__(self): - ''' 返回当前存储的量 - ''' - return len(self.buffer) - class DQN: - def __init__(self, n_states,n_actions,cfg): + def __init__(self,n_actions,model,memory,cfg): self.n_actions = n_actions self.device = torch.device(cfg.device) # cpu or cuda self.gamma = cfg.gamma # 奖励的折扣因子 # e-greedy策略相关参数 - self.frame_idx = 0 # 用于epsilon的衰减计数 - self.epsilon = lambda frame_idx: cfg.epsilon_end + \ - (cfg.epsilon_start - cfg.epsilon_end) * \ - math.exp(-1. * frame_idx / cfg.epsilon_decay) + self.sample_count = 0 # 用于epsilon的衰减计数 + self.epsilon = cfg.epsilon_start + self.sample_count = 0 + self.epsilon_start = cfg.epsilon_start + self.epsilon_end = cfg.epsilon_end + self.epsilon_decay = cfg.epsilon_decay self.batch_size = cfg.batch_size - self.policy_net = MLP(n_states,n_actions).to(self.device) - self.target_net = MLP(n_states,n_actions).to(self.device) + self.policy_net = model.to(self.device) + self.target_net = model.to(self.device) for target_param, param in zip(self.target_net.parameters(),self.policy_net.parameters()): # 复制参数到目标网路targe_net target_param.data.copy_(param.data) self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr) # 优化器 - self.memory = ReplayBuffer(cfg.memory_capacity) # 经验回放 + self.memory = memory # 经验回放 - def choose_action(self, state): + def sample(self, state): ''' 选择动作 ''' - self.frame_idx += 1 - if random.random() > self.epsilon(self.frame_idx): + self.sample_count += 1 + self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \ + math.exp(-1. * self.sample_count / self.epsilon_decay) # epsilon是会递减的，这里选择指数递减 + if random.random() > self.epsilon: with torch.no_grad(): state = torch.tensor(state, device=self.device, dtype=torch.float32).unsqueeze(dim=0) q_values = self.policy_net(state) @@ -92,11 +54,16 @@ class DQN: else: action = random.randrange(self.n_actions) return action + def predict(self,state): + with torch.no_grad(): + state = torch.tensor(state, device=self.device, dtype=torch.float32).unsqueeze(dim=0) + q_values = self.policy_net(state) + action = q_values.max(1)[1].item() # 选择Q值最大的动作 + return action def update(self): if len(self.memory) < self.batch_size: # 当memory中不满足一个批量时，不更新策略 return # 从经验回放中(replay memory)中随机采样一个批量的转移(transition) - # print('updating') state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample( self.batch_size) @@ -118,9 +85,11 @@ class DQN: self.optimizer.step() def save(self, path): - torch.save(self.target_net.state_dict(), path+'dqn_checkpoint.pth') + from pathlib import Path + Path(path).mkdir(parents=True, exist_ok=True) + torch.save(self.target_net.state_dict(), path+'checkpoint.pth') def load(self, path): - self.target_net.load_state_dict(torch.load(path+'dqn_checkpoint.pth')) + self.target_net.load_state_dict(torch.load(path+'checkpoint.pth')) for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()): param.data.copy_(target_param.data) diff --git a/projects/codes/DQN/dqn_cnn.py b/projects/codes/DQN/dqn_cnn.py deleted file mode 100644 index 4c086b2..0000000 --- a/projects/codes/DQN/dqn_cnn.py +++ /dev/null @@ -1,134 +0,0 @@ -import torch -import torch.nn as nn -import torch.optim as optim -import torch.autograd as autograd -import random -import math -class CNN(nn.Module): - def __init__(self, input_dim, output_dim): - super(CNN, self).__init__() - - self.input_dim = input_dim - self.output_dim = output_dim - - self.features = nn.Sequential( - nn.Conv2d(input_dim[0], 32, kernel_size=8, stride=4), - nn.ReLU(), - nn.Conv2d(32, 64, kernel_size=4, stride=2), - nn.ReLU(), - nn.Conv2d(64, 64, kernel_size=3, stride=1), - nn.ReLU() - ) - - self.fc = nn.Sequential( - nn.Linear(self.feature_size(), 512), - nn.ReLU(), - nn.Linear(512, self.output_dim) - ) - - def forward(self, x): - x = self.features(x) - x = x.view(x.size(0), -1) - x = self.fc(x) - return x - - def feature_size(self): - return self.features(autograd.Variable(torch.zeros(1, *self.input_dim))).view(1, -1).size(1) - - - def act(self, state, epsilon): - if random.random() > epsilon: - state = Variable(torch.FloatTensor(np.float32(state)).unsqueeze(0), volatile=True) - q_value = self.forward(state) - action = q_value.max(1)[1].data[0] - else: - action = random.randrange(env.action_space.n) - return action - -class ReplayBuffer: - def __init__(self, capacity): - self.capacity = capacity # 经验回放的容量 - self.buffer = [] # 缓冲区 - self.position = 0 - - def push(self, state, action, reward, next_state, done): - ''' 缓冲区是一个队列，容量超出时去掉开始存入的转移(transition) - ''' - if len(self.buffer) < self.capacity: - self.buffer.append(None) - self.buffer[self.position] = (state, action, reward, next_state, done) - self.position = (self.position + 1) % self.capacity - - def sample(self, batch_size): - batch = random.sample(self.buffer, batch_size) # 随机采出小批量转移 - state, action, reward, next_state, done = zip(*batch) # 解压成状态，动作等 - return state, action, reward, next_state, done - - def __len__(self): - ''' 返回当前存储的量 - ''' - return len(self.buffer) - -class DQN: - def __init__(self, n_states, n_actions, cfg): - - self.n_actions = n_actions # 总的动作个数 - self.device = cfg.device # 设备，cpu或gpu等 - self.gamma = cfg.gamma # 奖励的折扣因子 - # e-greedy策略相关参数 - self.frame_idx = 0 # 用于epsilon的衰减计数 - self.epsilon = lambda frame_idx: cfg.epsilon_end + \ - (cfg.epsilon_start - cfg.epsilon_end) * \ - math.exp(-1. * frame_idx / cfg.epsilon_decay) - self.batch_size = cfg.batch_size - self.policy_net = CNN(n_states, n_actions).to(self.device) - self.target_net = CNN(n_states, n_actions).to(self.device) - for target_param, param in zip(self.target_net.parameters(),self.policy_net.parameters()): # 复制参数到目标网路targe_net - target_param.data.copy_(param.data) - self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr) # 优化器 - self.memory = ReplayBuffer(cfg.memory_capacity) # 经验回放 - - def choose_action(self, state): - ''' 选择动作 - ''' - self.frame_idx += 1 - if random.random() > self.epsilon(self.frame_idx): - with torch.no_grad(): - print(type(state)) - state = torch.tensor([state], device=self.device, dtype=torch.float32) - q_values = self.policy_net(state) - action = q_values.max(1)[1].item() # 选择Q值最大的动作 - else: - action = random.randrange(self.n_actions) - return action - def update(self): - if len(self.memory) < self.batch_size: # 当memory中不满足一个批量时，不更新策略 - return - # 从经验回放中(replay memory)中随机采样一个批量的转移(transition) - state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample( - self.batch_size) - # 转为张量 - state_batch = torch.tensor(state_batch, device=self.device, dtype=torch.float) - action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(1) - reward_batch = torch.tensor(reward_batch, device=self.device, dtype=torch.float) - next_state_batch = torch.tensor(next_state_batch, device=self.device, dtype=torch.float) - done_batch = torch.tensor(np.float32(done_batch), device=self.device) - q_values = self.policy_net(state_batch).gather(dim=1, index=action_batch) # 计算当前状态(s_t,a)对应的Q(s_t, a) - next_q_values = self.target_net(next_state_batch).max(1)[0].detach() # 计算下一时刻的状态(s_t_,a)对应的Q值 - # 计算期望的Q值，对于终止状态，此时done_batch[0]=1, 对应的expected_q_value等于reward - expected_q_values = reward_batch + self.gamma * next_q_values * (1-done_batch) - loss = nn.MSELoss()(q_values, expected_q_values.unsqueeze(1)) # 计算均方根损失 - # 优化更新模型 - self.optimizer.zero_grad() - loss.backward() - for param in self.policy_net.parameters(): # clip防止梯度爆炸 - param.grad.data.clamp_(-1, 1) - self.optimizer.step() - - def save(self, path): - torch.save(self.target_net.state_dict(), path+'dqn_checkpoint.pth') - - def load(self, path): - self.target_net.load_state_dict(torch.load(path+'dqn_checkpoint.pth')) - for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()): - param.data.copy_(target_param.data) \ No newline at end of file diff --git a/projects/codes/DQN/dqn_cnn2.py b/projects/codes/DQN/dqn_cnn2.py deleted file mode 100644 index 67b7fd8..0000000 --- a/projects/codes/DQN/dqn_cnn2.py +++ /dev/null @@ -1,142 +0,0 @@ -import torch -import torch.nn as nn -import torch.optim as optim -import torch.autograd as autograd -import random -import math -import numpy as np -class CNN(nn.Module): - def __init__(self, n_frames, n_actions): - super(CNN,self).__init__() - self.n_frames = n_frames - self.n_actions = n_actions - - # Layers - self.conv1 = nn.Conv2d( - in_channels=n_frames, - out_channels=16, - kernel_size=8, - stride=4, - padding=2 - ) - self.conv2 = nn.Conv2d( - in_channels=16, - out_channels=32, - kernel_size=4, - stride=2, - padding=1 - ) - self.fc1 = nn.Linear( - in_features=3200, - out_features=256, - ) - self.fc2 = nn.Linear( - in_features=256, - out_features=n_actions, - ) - - # Activation Functions - self.relu = nn.ReLU() - - def flatten(self, x): - batch_size = x.size()[0] - x = x.view(batch_size, -1) - return x - - def forward(self, x): - - # Forward pass - x = self.relu(self.conv1(x)) # In: (80, 80, 4) Out: (20, 20, 16) - x = self.relu(self.conv2(x)) # In: (20, 20, 16) Out: (10, 10, 32) - x = self.flatten(x) # In: (10, 10, 32) Out: (3200,) - x = self.relu(self.fc1(x)) # In: (3200,) Out: (256,) - x = self.fc2(x) # In: (256,) Out: (4,) - - return x - -class ReplayBuffer: - def __init__(self, capacity): - self.capacity = capacity # 经验回放的容量 - self.buffer = [] # 缓冲区 - self.position = 0 - - def push(self, state, action, reward, next_state, done): - ''' 缓冲区是一个队列，容量超出时去掉开始存入的转移(transition) - ''' - if len(self.buffer) < self.capacity: - self.buffer.append(None) - self.buffer[self.position] = (state, action, reward, next_state, done) - self.position = (self.position + 1) % self.capacity - - def sample(self, batch_size): - batch = random.sample(self.buffer, batch_size) # 随机采出小批量转移 - state, action, reward, next_state, done = zip(*batch) # 解压成状态，动作等 - return state, action, reward, next_state, done - - def __len__(self): - ''' 返回当前存储的量 - ''' - return len(self.buffer) - -class DQN: - def __init__(self, n_states, n_actions, cfg): - - self.n_actions = n_actions # 总的动作个数 - self.device = cfg.device # 设备，cpu或gpu等 - self.gamma = cfg.gamma # 奖励的折扣因子 - # e-greedy策略相关参数 - self.frame_idx = 0 # 用于epsilon的衰减计数 - self.epsilon = lambda frame_idx: cfg.epsilon_end + \ - (cfg.epsilon_start - cfg.epsilon_end) * \ - math.exp(-1. * frame_idx / cfg.epsilon_decay) - self.batch_size = cfg.batch_size - self.policy_net = CNN(n_states, n_actions).to(self.device) - self.target_net = CNN(n_states, n_actions).to(self.device) - for target_param, param in zip(self.target_net.parameters(),self.policy_net.parameters()): # 复制参数到目标网路targe_net - target_param.data.copy_(param.data) - self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr) # 优化器 - self.memory = ReplayBuffer(cfg.memory_capacity) # 经验回放 - - def choose_action(self, state): - ''' 选择动作 - ''' - self.frame_idx += 1 - if random.random() > self.epsilon(self.frame_idx): - with torch.no_grad(): - state = torch.tensor([state], device=self.device, dtype=torch.float32) - q_values = self.policy_net(state) - action = q_values.max(1)[1].item() # 选择Q值最大的动作 - else: - action = random.randrange(self.n_actions) - return action - def update(self): - if len(self.memory) < self.batch_size: # 当memory中不满足一个批量时，不更新策略 - return - # 从经验回放中(replay memory)中随机采样一个批量的转移(transition) - state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample( - self.batch_size) - # 转为张量 - state_batch = torch.tensor(state_batch, device=self.device, dtype=torch.float) - action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(1) - reward_batch = torch.tensor(reward_batch, device=self.device, dtype=torch.float) - next_state_batch = torch.tensor(next_state_batch, device=self.device, dtype=torch.float) - done_batch = torch.tensor(np.float32(done_batch), device=self.device) - q_values = self.policy_net(state_batch).gather(dim=1, index=action_batch) # 计算当前状态(s_t,a)对应的Q(s_t, a) - next_q_values = self.target_net(next_state_batch).max(1)[0].detach() # 计算下一时刻的状态(s_t_,a)对应的Q值 - # 计算期望的Q值，对于终止状态，此时done_batch[0]=1, 对应的expected_q_value等于reward - expected_q_values = reward_batch + self.gamma * next_q_values * (1-done_batch) - loss = nn.MSELoss()(q_values, expected_q_values.unsqueeze(1)) # 计算均方根损失 - # 优化更新模型 - self.optimizer.zero_grad() - loss.backward() - for param in self.policy_net.parameters(): # clip防止梯度爆炸 - param.grad.data.clamp_(-1, 1) - self.optimizer.step() - - def save(self, path): - torch.save(self.target_net.state_dict(), path+'dqn_checkpoint.pth') - - def load(self, path): - self.target_net.load_state_dict(torch.load(path+'dqn_checkpoint.pth')) - for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()): - param.data.copy_(target_param.data) \ No newline at end of file diff --git a/projects/codes/DQN/outputs/CartPole-v0/20220713-211653/models/dqn_checkpoint.pth 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zf&&-6e_l-PgE}LXzAoA2U+ht&2@BpCi1Ih9XyvuxeLW`icUcP+! z#x{qTb~RmHT?jzK3Fi%g8D&susi`7}?#eKg{9~?#(_O>f!Qm9f60%M$pgKr2>v6pY zcDQfl=OoGKwHIqSjuzt^EC|E@>PhO%{WZ-4blqltvq N{=LR}4|S;F{{ay}nPC6` literal 0 HcmV?d00001 diff --git a/projects/codes/DoubleDQN/task0.py b/projects/codes/DoubleDQN/task0.py index 66dfcd9..7451d24 100644 --- a/projects/codes/DoubleDQN/task0.py +++ b/projects/codes/DoubleDQN/task0.py @@ -20,31 +20,33 @@ import argparse from common.utils import save_results,make_dir from common.utils import plot_rewards,save_args +from common.models import MLP +from common.memories import ReplayBuffer from DoubleDQN.double_dqn import DoubleDQN def get_args(): - """ Hyperparameters + """ 超参数 """ - curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # Obtain current time + curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间 parser = argparse.ArgumentParser(description="hyperparameters") parser.add_argument('--algo_name',default='DoubleDQN',type=str,help="name of algorithm") parser.add_argument('--env_name',default='CartPole-v0',type=str,help="name of environment") parser.add_argument('--train_eps',default=200,type=int,help="episodes of training") parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing") - parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor") + parser.add_argument('--gamma',default=0.95,type=float,help="discounted factor") parser.add_argument('--epsilon_start',default=0.95,type=float,help="initial value of epsilon") parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon") parser.add_argument('--epsilon_decay',default=500,type=int,help="decay rate of epsilon") parser.add_argument('--lr',default=0.0001,type=float,help="learning rate") parser.add_argument('--memory_capacity',default=100000,type=int,help="memory capacity") parser.add_argument('--batch_size',default=64,type=int) - parser.add_argument('--target_update',default=2,type=int) + parser.add_argument('--target_update',default=4,type=int) parser.add_argument('--hidden_dim',default=256,type=int) parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda") parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \ '/' + curr_time + '/results/' ) parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \ - '/' + curr_time + '/models/' ) # path to save models + '/' + curr_time + '/models/' ) # 保存模型的路径 parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not") args = parser.parse_args() return args @@ -55,19 +57,20 @@ def env_agent_config(cfg,seed=1): env.seed(seed) n_states = env.observation_space.shape[0] n_actions = env.action_space.n - agent = DoubleDQN(n_states,n_actions,cfg) + model = MLP(n_states, n_actions,hidden_dim=cfg.hidden_dim) + memory = ReplayBuffer(cfg.memory_capacity) + agent = DoubleDQN(n_states,n_actions,model,memory,cfg) return env,agent def train(cfg,env,agent): - print('Start training!') - print(f'Env:{cfg.env_name}, Algorithm:{cfg.algo_name}, Device:{cfg.device}') + print("开始训练！") + print(f"回合：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}") rewards = [] # 记录所有回合的奖励 - ma_rewards = [] # 记录所有回合的滑动平均奖励 for i_ep in range(cfg.train_eps): ep_reward = 0 # 记录一回合内的奖励 state = env.reset() # 重置环境，返回初始状态 while True: - action = agent.choose_action(state) + action = agent.sample(state) next_state, reward, done, _ = env.step(action) ep_reward += reward agent.memory.push(state, action, reward, next_state, done) @@ -78,61 +81,45 @@ def train(cfg,env,agent): if i_ep % cfg.target_update == 0: agent.target_net.load_state_dict(agent.policy_net.state_dict()) if (i_ep+1)%10 == 0: - print(f'Env:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.2f}') - rewards.append(ep_reward) - if ma_rewards: - ma_rewards.append( - 0.9*ma_rewards[-1]+0.1*ep_reward) - else: - ma_rewards.append(ep_reward) - print('Finish training!') - return {'rewards':rewards,'ma_rewards':ma_rewards} + print(f'回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward:.2f}，Epislon：{agent.epsilon:.3f}') + rewards.append(ep_reward) + print("完成训练！") + return {'rewards':rewards} def test(cfg,env,agent): - print('Start testing') - print(f'Env:{cfg.env_name}, Algorithm:{cfg.algo_name}, Device:{cfg.device}') - ############# 由于测试不需要使用epsilon-greedy策略，所以相应的值设置为0 ############### - cfg.epsilon_start = 0.0 # e-greedy策略中初始epsilon - cfg.epsilon_end = 0.0 # e-greedy策略中的终止epsilon - ################################################################################ + print("开始测试！") + print(f"回合：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}") rewards = [] # 记录所有回合的奖励 - ma_rewards = [] # 记录所有回合的滑动平均奖励 - for i_ep in range(cfg.test_eps): state = env.reset() ep_reward = 0 while True: - action = agent.choose_action(state) + action = agent.predict(state) next_state, reward, done, _ = env.step(action) state = next_state ep_reward += reward if done: break rewards.append(ep_reward) - if ma_rewards: - ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1) - else: - ma_rewards.append(ep_reward) - print(f"Epside:{i_ep+1}/{cfg.test_eps}, Reward:{ep_reward:.1f}") - print('Finish testing!') - return {'rewards':rewards,'ma_rewards':ma_rewards} + print(f'回合：{i_ep+1}/{cfg.test_eps}，奖励：{ep_reward:.2f}') + print("完成测试！") + return {'rewards':rewards} if __name__ == "__main__": cfg = get_args() - print(cfg.device) - # training - env,agent = env_agent_config(cfg,seed=1) + # 训练 + env, agent = env_agent_config(cfg,seed=1) res_dic = train(cfg, env, agent) - make_dir(cfg.result_path, cfg.model_path) - save_args(cfg) - agent.save(path=cfg.model_path) + make_dir(cfg.result_path, cfg.model_path) + save_args(cfg) # 保存参数 + agent.save(path=cfg.model_path) # 保存模型 save_results(res_dic, tag='train', path=cfg.result_path) - plot_rewards(res_dic['rewards'], res_dic['ma_rewards'], cfg, tag="train") - # testing - env,agent = env_agent_config(cfg,seed=10) - agent.load(path=cfg.model_path) - res_dic = test(cfg,env,agent) + plot_rewards(res_dic['rewards'], cfg, tag="train") + # 测试 + env, agent = env_agent_config(cfg,seed=1) + agent.load(path=cfg.model_path) # 导入模型 + res_dic = test(cfg, env, agent) save_results(res_dic, tag='test', - path=cfg.result_path) - plot_rewards(res_dic['rewards'], res_dic['ma_rewards'], cfg, tag="test") + path=cfg.result_path) # 保存结果 + plot_rewards(res_dic['rewards'], cfg, tag="test") # 画出结果 diff --git a/projects/codes/MonteCarlo/agent.py b/projects/codes/MonteCarlo/agent.py index bfe6940..fe2a287 100644 --- a/projects/codes/MonteCarlo/agent.py +++ b/projects/codes/MonteCarlo/agent.py @@ -5,7 +5,7 @@ Author: John Email: johnjim0816@gmail.com Date: 2021-03-12 16:14:34 LastEditor: John -LastEditTime: 2021-05-05 16:58:39 +LastEditTime: 2022-08-15 18:10:13 Discription: Environment: ''' @@ -22,11 +22,10 @@ class FisrtVisitMC: self.epsilon = cfg.epsilon self.gamma = cfg.gamma self.Q_table = defaultdict(lambda: np.zeros(n_actions)) - self.returns_sum = defaultdict(float) # sum of returns + self.returns_sum = defaultdict(float) # 保存return之和 self.returns_count = defaultdict(float) - def choose_action(self,state): - ''' e-greed policy ''' + def sample(self,state): if state in self.Q_table.keys(): best_action = np.argmax(self.Q_table[state]) action_probs = np.ones(self.n_actions, dtype=float) * self.epsilon / self.n_actions @@ -35,6 +34,15 @@ class FisrtVisitMC: else: action = np.random.randint(0,self.n_actions) return action + def predict(self,state): + if state in self.Q_table.keys(): + best_action = np.argmax(self.Q_table[state]) + action_probs = np.ones(self.n_actions, dtype=float) * self.epsilon / self.n_actions + action_probs[best_action] += (1.0 - self.epsilon) + action = np.argmax(self.Q_table[state]) + else: + action = np.random.randint(0,self.n_actions) + return action def update(self,one_ep_transition): # Find all (state, action) pairs we've visited in this one_ep_transition # We convert each state to a tuple so that we can use it as a dict key @@ -50,16 +58,18 @@ class FisrtVisitMC: self.returns_sum[sa_pair] += G self.returns_count[sa_pair] += 1.0 self.Q_table[state][action] = self.returns_sum[sa_pair] / self.returns_count[sa_pair] - def save(self,path): + def save(self,path=None): '''把 Q表格 的数据保存到文件中 ''' + from pathlib import Path + Path(path).mkdir(parents=True, exist_ok=True) torch.save( obj=self.Q_table, f=path+"Q_table", pickle_module=dill ) - def load(self, path): + def load(self, path=None): '''从文件中读取数据到 Q表格 ''' self.Q_table =torch.load(f=path+"Q_table",pickle_module=dill) \ No newline at end of file diff --git a/projects/codes/MonteCarlo/outputs/Racetrack/20210505-165945/models/Q_table b/projects/codes/MonteCarlo/outputs/Racetrack/20210505-165945/models/Q_table deleted file mode 100644 index 6205ee5b56fe21e32d7dd82543f0cdd4fcebaf43..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 105647 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z#~`5Pb2xo>MtZs(c(vjmhXw~JJ4eTSujYrxwMg z&s9KBjS9-CudhC!9HyUU%^R><19YFG{$CWgvkz=<<4ML9SvTb<$;ruC$_N1<;54qO zsXx+;MDBa*B79?M)Q}V!0S)<4S9_ZUATC$IzkzPgcn7n zoJd!Yd#IYiL?6v^ffB8MLc%(r5iqi;pz-E!MkK7tiH4bV_Giv?{u%C89oS@5aS>+4 zacaU9&IkVd1fqKC)nJhX=)&B*yahOy#@}inFkmVTlNKA$(9nRkAsM8tyW^`X9`0a6 zs7LFm@q7KR;~Kd0!u#Y3H+N&(7!Jxu_a-3^fu6qg2Fd zt%%zFj3V;6cu0ppVJ2FM$Uix1J03?zGx2UZhqOKfj*)CelIO4lp20nyo;k-l%8QD8 z(US!>)sCl6pML+Ey#SCbR&1zbgJ{rZkG zx#Npr)8L3W=!1g}=te4rDM|F+y^$Rh z78bHm3A!IqDXe>WdMP@#(zCK)S(bt;phRjF_WR~8TYD9d;1##{XS8$ATfiLA+L-Yl tKp;7J#64cRH6vE>uSn&8ym*R>{ma~2_Cfn!QuyEAT?ck1Za2B`{{R=w;l2O> literal 0 HcmV?d00001 diff --git a/projects/codes/MonteCarlo/task0.py b/projects/codes/MonteCarlo/task0.py new file mode 100644 index 0000000..fbddf0a --- /dev/null +++ b/projects/codes/MonteCarlo/task0.py @@ -0,0 +1,110 @@ +#!/usr/bin/env python +# coding=utf-8 +''' +Author: John +Email: johnjim0816@gmail.com +Date: 2021-03-11 14:26:44 +LastEditor: John +LastEditTime: 2022-08-15 18:12:13 +Discription: +Environment: +''' +import sys,os +curr_path = os.path.dirname(os.path.abspath(__file__)) # 当前文件所在绝对路径 +parent_path = os.path.dirname(curr_path) # 父路径 +sys.path.append(parent_path) # 添加路径到系统路径 + +import datetime +import argparse +from common.utils import save_results,save_args,plot_rewards + +from MonteCarlo.agent import FisrtVisitMC +from envs.racetrack_env import RacetrackEnv + +curr_time = datetime.datetime.now().strftime( + "%Y%m%d-%H%M%S") # obtain current time + +def get_args(): + """ 超参数 + """ + curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间 + parser = argparse.ArgumentParser(description="hyperparameters") + parser.add_argument('--algo_name',default='First-Visit MC',type=str,help="name of algorithm") + parser.add_argument('--env_name',default='Racetrack',type=str,help="name of environment") + parser.add_argument('--train_eps',default=200,type=int,help="episodes of training") + parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing") + parser.add_argument('--gamma',default=0.9,type=float,help="discounted factor") + parser.add_argument('--epsilon',default=0.15,type=float,help="the probability to select a random action") + parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda") + parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \ + '/' + curr_time + '/results/' ) + parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \ + '/' + curr_time + '/models/' ) + parser.add_argument('--show_fig',default=False,type=bool,help="if show figure or not") + parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not") + args = parser.parse_args() + return args + +def env_agent_config(cfg,seed=1): + env = RacetrackEnv() + n_actions = env.action_space.n + agent = FisrtVisitMC(n_actions, cfg) + return env,agent + +def train(cfg, env, agent): + print("开始训练！") + print(f"环境：{cfg.env_name}，算法：{cfg.algo_name}，设备：{cfg.device}") + rewards = [] + for i_ep in range(cfg.train_eps): + state = env.reset() + ep_reward = 0 + one_ep_transition = [] + while True: + action = agent.sample(state) + next_state, reward, done = env.step(action) + ep_reward += reward + one_ep_transition.append((state, action, reward)) + state = next_state + if done: + break + rewards.append(ep_reward) + agent.update(one_ep_transition) + if (i_ep+1) % 10 == 0: + print(f"Episode:{i_ep+1}/{cfg.train_eps}: Reward:{ep_reward}") + print("完成训练") + return {'rewards':rewards} + +def test(cfg, env, agent): + print("开始测试！") + print(f"环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}") + rewards = [] + for i_ep in range(cfg.test_eps): + state = env.reset() + ep_reward = 0 + while True: + action = agent.predict(state) + next_state, reward, done = env.step(action) + ep_reward += reward + state = next_state + if done: + break + rewards.append(ep_reward) + print(f'回合：{i_ep+1}/{cfg.test_eps}，奖励：{ep_reward:.2f}') + return {'rewards':rewards} + +if __name__ == "__main__": + cfg = get_args() + # 训练 + env, agent = env_agent_config(cfg) + res_dic = train(cfg, env, agent) + save_args(cfg,path = cfg.result_path) # 保存参数到模型路径上 + agent.save(path = cfg.model_path) # 保存模型 + save_results(res_dic, tag = 'train', path = cfg.result_path) + plot_rewards(res_dic['rewards'], cfg, path = cfg.result_path,tag = "train") + # 测试 + env, agent = env_agent_config(cfg) # 也可以不加，加这一行的是为了避免训练之后环境可能会出现问题，因此新建一个环境用于测试 + agent.load(path = cfg.model_path) # 导入模型 + res_dic = test(cfg, env, agent) + save_results(res_dic, tag='test', + path = cfg.result_path) # 保存结果 + plot_rewards(res_dic['rewards'], cfg, path = cfg.result_path,tag = "test") # 画出结果 diff --git a/projects/codes/MonteCarlo/task0_train.py b/projects/codes/MonteCarlo/task0_train.py deleted file mode 100644 index 51858f8..0000000 --- a/projects/codes/MonteCarlo/task0_train.py +++ /dev/null @@ -1,118 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 -''' -Author: John -Email: johnjim0816@gmail.com -Date: 2021-03-11 14:26:44 -LastEditor: John -LastEditTime: 2021-05-05 17:27:50 -Discription: -Environment: -''' - -import sys,os -curr_path = os.path.dirname(__file__) -parent_path = os.path.dirname(curr_path) -sys.path.append(parent_path) # add current terminal path to sys.path - -import torch -import datetime - -from common.utils import save_results,make_dir -from common.plot import plot_rewards -from MonteCarlo.agent import FisrtVisitMC -from envs.racetrack_env import RacetrackEnv - -curr_time = datetime.datetime.now().strftime( - "%Y%m%d-%H%M%S") # obtain current time - -class MCConfig: - def __init__(self): - self.algo = "MC" # name of algo - self.env = 'Racetrack' - self.result_path = curr_path+"/outputs/" + self.env + \ - '/'+curr_time+'/results/' # path to save results - self.model_path = curr_path+"/outputs/" + self.env + \ - '/'+curr_time+'/models/' # path to save models - # epsilon: The probability to select a random action . - self.epsilon = 0.15 - self.gamma = 0.9 # gamma: Gamma discount factor. - self.train_eps = 200 - self.device = torch.device( - "cuda" if torch.cuda.is_available() else "cpu") # check gpu - -def env_agent_config(cfg,seed=1): - env = RacetrackEnv() - n_actions = 9 - agent = FisrtVisitMC(n_actions, cfg) - return env,agent - -def train(cfg, env, agent): - print('Start to eval !') - print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}') - rewards = [] - ma_rewards = [] # moving average rewards - for i_ep in range(cfg.train_eps): - state = env.reset() - ep_reward = 0 - one_ep_transition = [] - while True: - action = agent.choose_action(state) - next_state, reward, done = env.step(action) - ep_reward += reward - one_ep_transition.append((state, action, reward)) - state = next_state - if done: - break - rewards.append(ep_reward) - if ma_rewards: - ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1) - else: - ma_rewards.append(ep_reward) - agent.update(one_ep_transition) - if (i_ep+1) % 10 == 0: - print(f"Episode:{i_ep+1}/{cfg.train_eps}: Reward:{ep_reward}") - print('Complete training！') - return rewards, ma_rewards - -def eval(cfg, env, agent): - print('Start to eval !') - print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}') - rewards = [] - ma_rewards = [] # moving average rewards - for i_ep in range(cfg.train_eps): - state = env.reset() - ep_reward = 0 - while True: - action = agent.choose_action(state) - next_state, reward, done = env.step(action) - ep_reward += reward - state = next_state - if done: - break - rewards.append(ep_reward) - if ma_rewards: - ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1) - else: - ma_rewards.append(ep_reward) - if (i_ep+1) % 10 == 0: - print(f"Episode:{i_ep+1}/{cfg.train_eps}: Reward:{ep_reward}") - return rewards, ma_rewards - -if __name__ == "__main__": - cfg = MCConfig() - - # train - env,agent = env_agent_config(cfg,seed=1) - rewards, ma_rewards = train(cfg, env, agent) - make_dir(cfg.result_path, cfg.model_path) - agent.save(path=cfg.model_path) - save_results(rewards, ma_rewards, tag='train', path=cfg.result_path) - plot_rewards(rewards, ma_rewards, tag="train", - algo=cfg.algo, path=cfg.result_path) - # eval - env,agent = env_agent_config(cfg,seed=10) - agent.load(path=cfg.model_path) - rewards,ma_rewards = eval(cfg,env,agent) - save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path) - plot_rewards(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path) diff --git a/projects/codes/QLearning/outputs/CliffWalking-v0/20220210-005501/models/Qleaning_model.pkl b/projects/codes/QLearning/outputs/CliffWalking-v0/20220210-005501/models/Qleaning_model.pkl deleted file mode 100644 index 9053e52073129edb7e6d058e6596d019edde62c1..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 5359 zcmZ{o30xG%wuc8%Fn|ka#NB921Qn1SqRB;L+(rp06_KdpFpPi>Gn}H)ixD%5EQ%Y7 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zAAijnvSuHdUd)lm-&u>sSxH63bG^O0jf{=kBK$T}VE#z1vb5|0-^e<%q$e~JL_YPL*wMOg1uW;~ zO|ld~;iXHLjuwB|&^k4>YTpBG_hO0Ffml*;*RIJ3a28KE;9<=^?V(TthU4U?SE&K} zNZ}t$a;L`4ef|7KCZ^OhG+x!$)1U6_92<+(lPs&Nq~t2F#gvsU-PY7IoM8)PX$6G zn>X~NsoW{74U7^xO%SYU{_x>rUtizW=?oioDQTL3f&UV`iGq^Saut>Gswy@vE-un~ zq8isXICwVf)4)LI`1s}i{yhda_+?~dxOsR`bVR?D5LjDN)Aa7$LJ{ty*Zrmftf{Xz z?%2^0>SyENu=CI%flIZID=L^F%IBpir=%2F*?(x|$B!RH=FKbb@rD3PT3TAUr^f3K zAH;Ka9XOz3Y;3&A#H7=^fIYMV6ee3uP4%{JolDu>iT(I-m%pyW;Nak`^stw1JKfw) z$HvCK_(>1uXBuA动作->状态-动作值（Q值）的映射，即Q表 - def choose_action(self, state): + def sample(self, state): + ''' 采样动作，训练时用 + ''' self.sample_count += 1 self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \ math.exp(-1. * self.sample_count / self.epsilon_decay) # epsilon是会递减的，这里选择指数递减 @@ -37,6 +39,8 @@ class QLearning(object): action = np.random.choice(self.n_actions) # 随机选择动作 return action def predict(self,state): + ''' 预测或选择动作，测试时用 + ''' action = np.argmax(self.Q_table[str(state)]) return action def update(self, state, action, reward, next_state, done): diff --git a/projects/codes/QLearning/task0.py b/projects/codes/QLearning/task0.py index 98e620e..15100d5 100644 --- a/projects/codes/QLearning/task0.py +++ b/projects/codes/QLearning/task0.py @@ -5,7 +5,7 @@ Author: John Email: johnjim0816@gmail.com Date: 2020-09-11 23:03:00 LastEditor: John -LastEditTime: 2022-06-21 19:36:05 +LastEditTime: 2022-08-10 11:25:56 Discription: Environment: ''' @@ -18,54 +18,45 @@ sys.path.append(parent_path) # 添加路径到系统路径 import gym import torch import datetime - -from env.gridworld_env import CliffWalkingWapper +import argparse +from envs.gridworld_env import CliffWalkingWapper from qlearning import QLearning -from common.utils import plot_rewards +from common.utils import plot_rewards,save_args from common.utils import save_results,make_dir +def get_args(): + """ + """ + curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间 + parser = argparse.ArgumentParser(description="hyperparameters") + parser.add_argument('--algo_name',default='Q-learning',type=str,help="name of algorithm") + parser.add_argument('--env_name',default='CliffWalking-v0',type=str,help="name of environment") + parser.add_argument('--train_eps',default=400,type=int,help="episodes of training") # 训练的回合数 + parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing") # 测试的回合数 + parser.add_argument('--gamma',default=0.90,type=float,help="discounted factor") # 折扣因子 + parser.add_argument('--epsilon_start',default=0.95,type=float,help="initial value of epsilon") # e-greedy策略中初始epsilon + parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon") # e-greedy策略中的终止epsilon + parser.add_argument('--epsilon_decay',default=300,type=int,help="decay rate of epsilon") # e-greedy策略中epsilon的衰减率 + parser.add_argument('--lr',default=0.1,type=float,help="learning rate") + parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda") + parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \ + '/' + curr_time + '/results/',type=str ) + parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \ + '/' + curr_time + '/models/',type=str,help="path to save models") + parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not") + args = parser.parse_args() + return args curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间 -class Config: - '''超参数 - ''' - - def __init__(self): - ################################## 环境超参数 ################################### - self.algo_name = 'Q-learning' # 算法名称 - self.env_name = 'CliffWalking-v0' # 环境名称 - self.device = torch.device( - "cuda" if torch.cuda.is_available() else "cpu") # 检测GPUgjgjlkhfsf风刀霜的撒发十 - self.seed = 10 # 随机种子，置0则不设置随机种子 - self.train_eps = 400 # 训练的回合数 - self.test_eps = 30 # 测试的回合数 - ################################################################################ - - ################################## 算法超参数 ################################### - self.gamma = 0.90 # 强化学习中的折扣因子 - self.epsilon_start = 0.95 # e-greedy策略中初始epsilon - self.epsilon_end = 0.01 # e-greedy策略中的终止epsilon - self.epsilon_decay = 300 # e-greedy策略中epsilon的衰减率 - self.lr = 0.1 # 学习率 - ################################################################################ - - ################################# 保存结果相关参数 ################################ - self.result_path = curr_path + "/outputs/" + self.env_name + \ - '/' + curr_time + '/results/' # 保存结果的路径 - self.model_path = curr_path + "/outputs/" + self.env_name + \ - '/' + curr_time + '/models/' # 保存模型的路径 - self.save = True # 是否保存图片 - ################################################################################ - + def train(cfg,env,agent): print('开始训练！') print(f'环境:{cfg.env_name}, 算法:{cfg.algo_name}, 设备:{cfg.device}') rewards = [] # 记录奖励 - ma_rewards = [] # 记录滑动平均奖励 for i_ep in range(cfg.train_eps): ep_reward = 0 # 记录每个回合的奖励 state = env.reset() # 重置环境,即开始新的回合 while True: - action = agent.choose_action(state) # 根据算法选择一个动作 + action = agent.sample(state) # 根据算法采样一个动作 next_state, reward, done, _ = env.step(action) # 与环境进行一次动作交互 agent.update(state, action, reward, next_state, done) # Q学习算法更新 state = next_state # 更新状态 @@ -73,19 +64,14 @@ def train(cfg,env,agent): if done: break rewards.append(ep_reward) - if ma_rewards: - ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1) - else: - ma_rewards.append(ep_reward) - print("回合数：{}/{}，奖励{:.1f}".format(i_ep+1, cfg.train_eps,ep_reward)) + print(f"回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward:.1f}，Epsilon：{agent.epsilon}") print('完成训练！') - return rewards,ma_rewards + return {"rewards":rewards} def test(cfg,env,agent): print('开始测试！') print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}') rewards = [] # 记录所有回合的奖励 - ma_rewards = [] # 滑动平均的奖励 for i_ep in range(cfg.test_eps): ep_reward = 0 # 记录每个episode的reward state = env.reset() # 重置环境, 重新开一局（即开始新的一个回合） @@ -97,13 +83,9 @@ def test(cfg,env,agent): if done: break rewards.append(ep_reward) - if ma_rewards: - ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1) - else: - ma_rewards.append(ep_reward) print(f"回合数：{i_ep+1}/{cfg.test_eps}, 奖励：{ep_reward:.1f}") print('完成测试！') - return rewards,ma_rewards + return {"rewards":rewards} def env_agent_config(cfg,seed=1): '''创建环境和智能体 @@ -119,23 +101,27 @@ def env_agent_config(cfg,seed=1): env.seed(seed) # 设置随机种子 n_states = env.observation_space.n # 状态维度 n_actions = env.action_space.n # 动作维度 - agent = QLearning(n_states,n_actions,cfg) + print(f"状态数：{n_states}，动作数：{n_actions}") + agent = QLearning(n_actions,cfg) return env,agent if __name__ == "__main__": - cfg = Config() + cfg = get_args() # 训练 - env, agent = env_agent_config(cfg, seed=1) - rewards, ma_rewards = train(cfg, env, agent) - make_dir(cfg.result_path, cfg.model_path) # 创建保存结果和模型路径的文件夹 - agent.save(path=cfg.model_path) # 保存模型 - save_results(rewards, ma_rewards, tag='train', - path=cfg.result_path) # 保存结果 - plot_rewards(rewards, ma_rewards, cfg, tag="train") # 画出结果 + env, agent = env_agent_config(cfg) + res_dic = train(cfg, env, agent) + make_dir(cfg.result_path, cfg.model_path) + save_args(cfg) # save parameters + agent.save(path=cfg.model_path) # save model + save_results(res_dic, tag='train', + path=cfg.result_path) + plot_rewards(res_dic['rewards'], cfg, tag="train") # 测试 - env, agent = env_agent_config(cfg, seed=10) + env, agent = env_agent_config(cfg) agent.load(path=cfg.model_path) # 导入模型 - rewards, ma_rewards = test(cfg, env, agent) - save_results(rewards, ma_rewards, tag='test', path=cfg.result_path) # 保存结果 - plot_rewards(rewards, ma_rewards, cfg, tag="test") # 画出结果 + res_dic = test(cfg, env, agent) + save_results(res_dic, tag='test', + path=cfg.result_path) # 保存结果 + plot_rewards(res_dic['rewards'], cfg, tag="test") # 画出结果 + diff --git 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https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2 - - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/ - - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge - - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ - - defaults -dependencies: - - backcall=0.2.0=pyh9f0ad1d_0 - - backports=1.0=py_2 - - backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0 - - blas=2.114=mkl - - blas-devel=3.9.0=14_win64_mkl - - brotlipy=0.7.0=py37hcc03f2d_1004 - - ca-certificates=2022.5.18.1=h5b45459_0 - - certifi=2022.5.18.1=py37h03978a9_0 - - cffi=1.15.0=py37hd8e9650_0 - - charset-normalizer=2.0.12=pyhd8ed1ab_0 - - colorama=0.4.4=pyh9f0ad1d_0 - - cryptography=37.0.1=py37h21b164f_0 - - cudatoolkit=11.3.1=h59b6b97_2 - - debugpy=1.6.0=py37hf2a7229_0 - - decorator=5.1.1=pyhd8ed1ab_0 - - entrypoints=0.4=pyhd8ed1ab_0 - - freetype=2.10.4=h546665d_1 - - idna=3.3=pyhd8ed1ab_0 - - intel-openmp=2022.1.0=h57928b3_3787 - - ipykernel=6.13.0=py37h90c5f73_0 - - jedi=0.18.1=py37h03978a9_1 - - jpeg=9e=h8ffe710_1 - - jupyter_client=7.3.1=pyhd8ed1ab_0 - - jupyter_core=4.10.0=py37h03978a9_0 - - lcms2=2.12=h2a16943_0 - - lerc=3.0=h0e60522_0 - - libblas=3.9.0=14_win64_mkl - - libcblas=3.9.0=14_win64_mkl - - libdeflate=1.10=h8ffe710_0 - - liblapack=3.9.0=14_win64_mkl - - liblapacke=3.9.0=14_win64_mkl - - libpng=1.6.37=h1d00b33_2 - - libsodium=1.0.18=h8d14728_1 - - libtiff=4.3.0=hc4061b1_4 - - libuv=1.43.0=h8ffe710_0 - - libwebp=1.2.2=h57928b3_0 - - libwebp-base=1.2.2=h8ffe710_1 - - libxcb=1.13=hcd874cb_1004 - - libzlib=1.2.11=h8ffe710_1014 - - lz4-c=1.9.3=h8ffe710_1 - - m2w64-gcc-libgfortran=5.3.0=6 - - m2w64-gcc-libs=5.3.0=7 - - m2w64-gcc-libs-core=5.3.0=7 - - m2w64-gmp=6.1.0=2 - - m2w64-libwinpthread-git=5.0.0.4634.697f757=2 - - matplotlib-inline=0.1.3=pyhd8ed1ab_0 - - mkl=2022.0.0=h0e2418a_796 - - mkl-devel=2022.0.0=h57928b3_797 - - mkl-include=2022.0.0=h0e2418a_796 - - msys2-conda-epoch=20160418=1 - - nest-asyncio=1.5.5=pyhd8ed1ab_0 - - numpy=1.21.6=py37h2830a78_0 - - openjpeg=2.4.0=hb211442_1 - - openssl=3.0.3=h8ffe710_0 - - packaging=21.3=pyhd8ed1ab_0 - - parso=0.8.3=pyhd8ed1ab_0 - - pickleshare=0.7.5=py_1003 - - pillow=9.1.1=py37h8675073_0 - - pip=22.1.1=pyhd8ed1ab_0 - - prompt-toolkit=3.0.29=pyha770c72_0 - - psutil=5.9.1=py37hcc03f2d_0 - - pthread-stubs=0.4=hcd874cb_1001 - - pycparser=2.21=pyhd8ed1ab_0 - - pyopenssl=22.0.0=pyhd8ed1ab_0 - - pysocks=1.7.1=py37h03978a9_5 - - python=3.7.12=h900ac77_100_cpython - - python-dateutil=2.8.2=pyhd8ed1ab_0 - - python_abi=3.7=2_cp37m - - pytorch=1.11.0=py3.7_cuda11.3_cudnn8_0 - - pytorch-mutex=1.0=cuda - - pywin32=303=py37hcc03f2d_0 - - pyzmq=23.0.0=py37hcce574b_0 - - requests=2.27.1=pyhd8ed1ab_0 - - setuptools=62.3.2=py37h03978a9_0 - - six=1.16.0=pyh6c4a22f_0 - - sqlite=3.38.5=h8ffe710_0 - - tbb=2021.5.0=h2d74725_1 - - tk=8.6.12=h8ffe710_0 - - torchaudio=0.11.0=py37_cu113 - - torchvision=0.12.0=py37_cu113 - - tornado=6.1=py37hcc03f2d_3 - - typing_extensions=4.2.0=pyha770c72_1 - - ucrt=10.0.20348.0=h57928b3_0 - - urllib3=1.26.9=pyhd8ed1ab_0 - - vc=14.2=hb210afc_6 - - vs2015_runtime=14.29.30037=h902a5da_6 - - wcwidth=0.2.5=pyh9f0ad1d_2 - - wheel=0.37.1=pyhd8ed1ab_0 - - win_inet_pton=1.1.0=py37h03978a9_4 - - xorg-libxau=1.0.9=hcd874cb_0 - - xorg-libxdmcp=1.1.3=hcd874cb_0 - - xz=5.2.5=h62dcd97_1 - - zeromq=4.3.4=h0e60522_1 - - zlib=1.2.11=h8ffe710_1014 - - zstd=1.5.2=h6255e5f_1 - - pip: - - cloudpickle==2.1.0 - - cycler==0.11.0 - - dill==0.3.4 - - easydict==1.9 - - fonttools==4.33.2 - - gym==0.21.0 - - importlib-metadata==4.12.0 - - ipython==7.32.0 - - kiwisolver==1.4.2 - - matplotlib==3.5.1 - - pandas==1.3.5 - - pygments==2.11.2 - - pyparsing==3.0.8 - - pytz==2022.1 - - scipy==1.7.3 - - seaborn==0.11.2 - - traitlets==5.1.1 - - zipp==3.8.1 -prefix: C:\Users\24438\anaconda3\envs\rl_tutorials diff --git a/projects/notebooks/1.QLearning.ipynb b/projects/notebooks/1.QLearning.ipynb new file mode 100644 index 0000000..c5cc3f7 --- /dev/null +++ b/projects/notebooks/1.QLearning.ipynb @@ -0,0 +1,922 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1、定义算法\n", + "强化学习算法的模式都比较固定，一般包括sample（即训练时采样动作），predict（测试时预测动作），update（算法更新）以及保存模型和加载模型等几个方法，其中对于每种算法samle和update的方式是不相同，而其他方法就大同小异。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import math\n", + "import torch\n", + "from collections import defaultdict\n", + "\n", + "class QLearning(object):\n", + " def __init__(self,n_states,\n", + " n_actions,cfg):\n", + " self.n_actions = n_actions \n", + " self.lr = cfg.lr # 学习率\n", + " self.gamma = cfg.gamma \n", + " self.epsilon = cfg.epsilon_start\n", + " self.sample_count = 0 \n", + " self.epsilon_start = cfg.epsilon_start\n", + " self.epsilon_end = cfg.epsilon_end\n", + " self.epsilon_decay = cfg.epsilon_decay\n", + " self.Q_table = defaultdict(lambda: np.zeros(n_actions)) # 用嵌套字典存放状态->动作->状态-动作值（Q值）的映射，即Q表\n", + " def sample(self, state):\n", + " ''' 采样动作，训练时用\n", + " '''\n", + " self.sample_count += 1\n", + " self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \\\n", + " math.exp(-1. * self.sample_count / self.epsilon_decay) # epsilon是会递减的，这里选择指数递减\n", + " # e-greedy 策略\n", + " if np.random.uniform(0, 1) > self.epsilon:\n", + " action = np.argmax(self.Q_table[str(state)]) # 选择Q(s,a)最大对应的动作\n", + " else:\n", + " action = np.random.choice(self.n_actions) # 随机选择动作\n", + " return action\n", + " def predict(self,state):\n", + " ''' 预测或选择动作，测试时用\n", + " '''\n", + " action = np.argmax(self.Q_table[str(state)])\n", + " return action\n", + " def update(self, state, action, reward, next_state, done):\n", + " Q_predict = self.Q_table[str(state)][action] \n", + " if done: # 终止状态\n", + " Q_target = reward \n", + " else:\n", + " Q_target = reward + self.gamma * np.max(self.Q_table[str(next_state)]) \n", + " self.Q_table[str(state)][action] += self.lr * (Q_target - Q_predict)\n", + " def save(self,path):\n", + " import dill\n", + " torch.save(\n", + " obj=self.Q_table,\n", + " f=path+\"Qleaning_model.pkl\",\n", + " pickle_module=dill\n", + " )\n", + " print(\"保存模型成功！\")\n", + " def load(self, path):\n", + " import dill\n", + " self.Q_table =torch.load(f=path+'Qleaning_model.pkl',pickle_module=dill)\n", + " print(\"加载模型成功！\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2、定义训练\n", + "强化学习算法的训练方式也比较固定，如下：\n", + "```python\n", + "for i_ep in range(train_eps): # 遍历每个回合\n", + " state = env.reset() # 重置环境,即开始新的回合\n", + " while True: # 对于一些比较复杂的游戏可以设置每回合最大的步长，例如while ep_step<100，就是每回合最大步长为100。\n", + " action = agent.sample(state) # 根据算法采样一个动作\n", + " next_state, reward, done, _ = env.step(action) # 与环境进行一次动作交互\n", + " agent.memory.push(state, action, reward, next_state, done) # 记录memory\n", + " agent.update(state, action, reward, next_state, done) # 算法更新\n", + " state = next_state # 更新状态\n", + " if done:\n", + " break\n", + "```\n", + "首先对于每个回合，回合开始时环境需要重置，好比我们每次开一把游戏需要从头再来一样。我们可以设置智能体在每回合数的最大步长，尤其是对于比较复杂的游戏，这样做的好处之一就是帮助智能体在训练中快速收敛，比如我们先验地知道最优解的大概步数，那么理论上智能体收敛时也应该是这个步数附近，设置最大步数可以方便智能体接近这个最优解。在每个回合中，智能体首先需要采样（sample），或者说采用探索策略例如常见的$\\varepsilon$-greedy策略或者UCB探索策略等等。采样的过程是将当前的状态state作为输入，智能体采样输出动作action。然后环境根据采样出来的动作反馈出下一个状态以及相应的reward等信息。接下来对于具有memory的智能体例如包含replay memory的DQN来说，需要将相应的transition（记住这个词，中文不好翻译，通常是状态、动作、奖励等信息）。紧接着就是智能体更新，对于深度强化学习此时一般从memory中随机采样一些transition进行更新，对于Q learning一般是采样上一次的transition。更新公式是比较关键的部分，但是也很通用，一般基于值的算法更新公式都是一个套路如下：\n", + "$$\n", + "y_{j}= \\begin{cases}r_{j} & \\text { for terminal } s_{t+1} \\\\ r_{j}+\\gamma \\max _{a^{\\prime}} Q\\left(s_{t+1}, a^{\\prime} ; \\theta\\right) & \\text { for non-terminal } s_{t+1}\\end{cases}\n", + "$$\n", + "智能体更新完之后，通常需要更新状态，即```state = next_state```，然后会检查是否完成了这一回合的游戏，即```done==True```，注意完成并不代表这回合成功，也有可能是失败的太离谱，等同学们有了自定义强化学习环境的经验就知道了（等你长大就知道了XD）。\n", + "如果需要记录奖励、损失等等的话可以再加上，如下方代码，实际项目中更多地使用tensorboard来记录相应的数据，甚至于笔者就在这些教学代码中使用过，但是看起来有些繁琐，容易给大家增加不必要的学习难度，因此学有余力以及需要在项目研究中做强化学习的可以去看看，也很简单。\n", + "此外稍微复杂一些的强化学习不是一次性写完代码就能收敛的，这时需要我们做一个调参侠。为了检查我们参数调得好不好，可以在终端print出奖励、损失以及epsilon等随着回合数的变化，这点说明一下强化学习的训练过程一般都是先探索然后收敛的，官方的话就是权衡exploration and exploitation。e-greedy策略的做法就是前期探索，然后逐渐减小探索率至慢慢收敛，也就是这个epsilon。这个值越大比如0.9就说明智能体90%的概率在随机探索，通常情况下会设置三个值，epsilon_start、epsilon_end以及epsilon_decay，即初始值、终止值和衰减率，其中初始值一般是0.95不变，终止值是0.01，也就是说即使在收敛阶段也让智能体保持很小概率的探索，这样做的原因就是智能体已经学出了一个不错的策略，但是保不齐还有更好的策略，好比我们知道要出人头地学历高比较重要，但是“人还是要有梦想的，万一实现了呢”，总是存在意外的可能，对吧。回归正题，比较关键的是epsilon_decay这个衰减率，这个epsilon衰减太快了学来的策略往往过拟合，好比一条只能选择一朵花的花道上，你早早选择了一朵看起来还可以的花，却错过了后面更多的好花。但是衰减的太慢会影响收敛的速度，好比你走过了花道的尽头也还没选出一朵花来，相比前者不如更甚。当然强化学习的调参相比于深度学习只能说是有过之无不及，比较复杂，不止epsilon这一个，这就需要同学们的耐心学习了。\n", + "强化学习测试的代码跟训练基本上是一样的，因此我放到同一个代码段里。相比于训练代码，测试代码主要有以下几点不同：1、测试模型的过程是不需要更新的，这个是不言而喻的；2、测试代码不需要采样（sample）动作，相比之代替的是预测（sample）动作，其区别就是采样动作时可能会使用各种策略例如$\\varepsilon$-greedy策略，而预测动作不需要，只需要根据训练时学习好的Q表或者网络模型代入状态得到动作即可；3、测试过程终端一般只需要看奖励，不需要看epislon等，反正它在测试中也是无意义的。" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def train(cfg,env,agent):\n", + " print('开始训练！')\n", + " print(f'环境:{cfg.env_name}, 算法:{cfg.algo_name}, 设备:{cfg.device}')\n", + " rewards = [] # 记录奖励\n", + " for i_ep in range(cfg.train_eps):\n", + " ep_reward = 0 # 记录每个回合的奖励\n", + " state = env.reset() # 重置环境,即开始新的回合\n", + " while True:\n", + " action = agent.sample(state) # 根据算法采样一个动作\n", + " next_state, reward, done, _ = env.step(action) # 与环境进行一次动作交互\n", + " agent.update(state, action, reward, next_state, done) # Q学习算法更新\n", + " state = next_state # 更新状态\n", + " ep_reward += reward\n", + " if done:\n", + " break\n", + " rewards.append(ep_reward)\n", + " print(f\"回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward:.1f}，Epsilon：{agent.epsilon}\")\n", + " print('完成训练！')\n", + " return {\"rewards\":rewards}\n", + "def test(cfg,env,agent):\n", + " print('开始测试！')\n", + " print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')\n", + " rewards = [] # 记录所有回合的奖励\n", + " for i_ep in range(cfg.test_eps):\n", + " ep_reward = 0 # 记录每个episode的reward\n", + " state = env.reset() # 重置环境, 重新开一局（即开始新的一个回合）\n", + " while True:\n", + " action = agent.predict(state) # 根据算法选择一个动作\n", + " next_state, reward, done, _ = env.step(action) # 与环境进行一个交互\n", + " state = next_state # 更新状态\n", + " ep_reward += reward\n", + " if done:\n", + " break\n", + " rewards.append(ep_reward)\n", + " print(f\"回合数：{i_ep+1}/{cfg.test_eps}, 奖励：{ep_reward:.1f}\")\n", + " print('完成测试！')\n", + " return {\"rewards\":rewards}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3、定义环境\n", + "\n", + "OpenAI Gym中其实集成了很多强化学习环境，足够大家学习了，但是在做强化学习的应用中免不了要自己创建环境，比如在本项目中其实不太好找到Qlearning能学出来的环境，Qlearning实在是太弱了，需要足够简单的环境才行，因此本项目写了一个环境，大家感兴趣的话可以看一下，一般环境接口最关键的部分即使reset和step。" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import gym\n", + "import turtle\n", + "import numpy as np\n", + "\n", + "# turtle tutorial : https://docs.python.org/3.3/library/turtle.html\n", + "\n", + "def GridWorld(gridmap=None, is_slippery=False):\n", + " if gridmap is None:\n", + " gridmap = ['SFFF', 'FHFH', 'FFFH', 'HFFG']\n", + " env = gym.make(\"FrozenLake-v0\", desc=gridmap, is_slippery=False)\n", + " env = FrozenLakeWapper(env)\n", + " return env\n", + "\n", + "\n", + "class FrozenLakeWapper(gym.Wrapper):\n", + " def __init__(self, env):\n", + " gym.Wrapper.__init__(self, env)\n", + " self.max_y = env.desc.shape[0]\n", + " self.max_x = env.desc.shape[1]\n", + " self.t = None\n", + " self.unit = 50\n", + "\n", + " def draw_box(self, x, y, fillcolor='', line_color='gray'):\n", + " self.t.up()\n", + " self.t.goto(x * self.unit, y * self.unit)\n", + " self.t.color(line_color)\n", + " self.t.fillcolor(fillcolor)\n", + " self.t.setheading(90)\n", + " self.t.down()\n", + " self.t.begin_fill()\n", + " for _ in range(4):\n", + " self.t.forward(self.unit)\n", + " self.t.right(90)\n", + " self.t.end_fill()\n", + "\n", + " def move_player(self, x, y):\n", + " self.t.up()\n", + " self.t.setheading(90)\n", + " self.t.fillcolor('red')\n", + " self.t.goto((x + 0.5) * self.unit, (y + 0.5) * self.unit)\n", + "\n", + " def render(self):\n", + " if self.t == None:\n", + " self.t = turtle.Turtle()\n", + " self.wn = turtle.Screen()\n", + " self.wn.setup(self.unit * self.max_x + 100,\n", + " self.unit * self.max_y + 100)\n", + " self.wn.setworldcoordinates(0, 0, self.unit * self.max_x,\n", + " self.unit * self.max_y)\n", + " self.t.shape('circle')\n", + " self.t.width(2)\n", + " self.t.speed(0)\n", + " self.t.color('gray')\n", + " for i in range(self.desc.shape[0]):\n", + " for j in range(self.desc.shape[1]):\n", + " x = j\n", + " y = self.max_y - 1 - i\n", + " if self.desc[i][j] == b'S': # Start\n", + " self.draw_box(x, y, 'white')\n", + " elif self.desc[i][j] == b'F': # Frozen ice\n", + " self.draw_box(x, y, 'white')\n", + " elif self.desc[i][j] == b'G': # Goal\n", + " self.draw_box(x, y, 'yellow')\n", + " elif self.desc[i][j] == b'H': # Hole\n", + " self.draw_box(x, y, 'black')\n", + " else:\n", + " self.draw_box(x, y, 'white')\n", + " self.t.shape('turtle')\n", + "\n", + " x_pos = self.s % self.max_x\n", + " y_pos = self.max_y - 1 - int(self.s / self.max_x)\n", + " self.move_player(x_pos, y_pos)\n", + "\n", + "\n", + "class CliffWalkingWapper(gym.Wrapper):\n", + " def __init__(self, env):\n", + " gym.Wrapper.__init__(self, env)\n", + " self.t = None\n", + " self.unit = 50\n", + " self.max_x = 12\n", + " self.max_y = 4\n", + "\n", + " def draw_x_line(self, y, x0, x1, color='gray'):\n", + " assert x1 > x0\n", + " self.t.color(color)\n", + " self.t.setheading(0)\n", + " self.t.up()\n", + " self.t.goto(x0, y)\n", + " self.t.down()\n", + " self.t.forward(x1 - x0)\n", + "\n", + " def draw_y_line(self, x, y0, y1, color='gray'):\n", + " assert y1 > y0\n", + " self.t.color(color)\n", + " self.t.setheading(90)\n", + " self.t.up()\n", + " self.t.goto(x, y0)\n", + " self.t.down()\n", + " self.t.forward(y1 - y0)\n", + "\n", + " def draw_box(self, x, y, fillcolor='', line_color='gray'):\n", + " self.t.up()\n", + " self.t.goto(x * self.unit, y * self.unit)\n", + " self.t.color(line_color)\n", + " self.t.fillcolor(fillcolor)\n", + " self.t.setheading(90)\n", + " self.t.down()\n", + " self.t.begin_fill()\n", + " for i in range(4):\n", + " self.t.forward(self.unit)\n", + " self.t.right(90)\n", + " self.t.end_fill()\n", + "\n", + " def move_player(self, x, y):\n", + " self.t.up()\n", + " self.t.setheading(90)\n", + " self.t.fillcolor('red')\n", + " self.t.goto((x + 0.5) * self.unit, (y + 0.5) * self.unit)\n", + "\n", + " def render(self):\n", + " if self.t == None:\n", + " self.t = turtle.Turtle()\n", + " self.wn = turtle.Screen()\n", + " self.wn.setup(self.unit * self.max_x + 100,\n", + " self.unit * self.max_y + 100)\n", + " self.wn.setworldcoordinates(0, 0, self.unit * self.max_x,\n", + " self.unit * self.max_y)\n", + " self.t.shape('circle')\n", + " self.t.width(2)\n", + " self.t.speed(0)\n", + " self.t.color('gray')\n", + " for _ in range(2):\n", + " self.t.forward(self.max_x * self.unit)\n", + " self.t.left(90)\n", + " self.t.forward(self.max_y * self.unit)\n", + " self.t.left(90)\n", + " for i in range(1, self.max_y):\n", + " self.draw_x_line(\n", + " y=i * self.unit, x0=0, x1=self.max_x * self.unit)\n", + " for i in range(1, self.max_x):\n", + " self.draw_y_line(\n", + " x=i * self.unit, y0=0, y1=self.max_y * self.unit)\n", + "\n", + " for i in range(1, self.max_x - 1):\n", + " self.draw_box(i, 0, 'black')\n", + " self.draw_box(self.max_x - 1, 0, 'yellow')\n", + " self.t.shape('turtle')\n", + "\n", + " x_pos = self.s % self.max_x\n", + " y_pos = self.max_y - 1 - int(self.s / self.max_x)\n", + " self.move_player(x_pos, y_pos)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import gym\n", + "def env_agent_config(cfg,seed=1):\n", + " '''创建环境和智能体\n", + " Args:\n", + " cfg ([type]): [description]\n", + " seed (int, optional): 随机种子. Defaults to 1.\n", + " Returns:\n", + " env [type]: 环境\n", + " agent : 智能体\n", + " ''' \n", + " env = gym.make(cfg.env_name) \n", + " env = CliffWalkingWapper(env)\n", + " env.seed(seed) # 设置随机种子\n", + " n_states = env.observation_space.n # 状态维度\n", + " n_actions = env.action_space.n # 动作维度\n", + " agent = QLearning(n_states,n_actions,cfg)\n", + " return env,agent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4、设置参数\n", + "\n", + "到这里所有qlearning模块就算完成了，下面需要设置一些参数，方便大家“炼丹”，其中默认的是笔者已经调好的～。另外为了定义了一个画图函数，用来描述奖励的变化。" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import datetime\n", + "import argparse\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "def get_args():\n", + " \"\"\" \n", + " \"\"\"\n", + " curr_time = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\") # 获取当前时间\n", + " parser = argparse.ArgumentParser(description=\"hyperparameters\") \n", + " parser.add_argument('--algo_name',default='Q-learning',type=str,help=\"name of algorithm\")\n", + " parser.add_argument('--env_name',default='CliffWalking-v0',type=str,help=\"name of environment\")\n", + " parser.add_argument('--train_eps',default=400,type=int,help=\"episodes of training\") # 训练的回合数\n", + " parser.add_argument('--test_eps',default=20,type=int,help=\"episodes of testing\") # 测试的回合数\n", + " parser.add_argument('--gamma',default=0.90,type=float,help=\"discounted factor\") # 折扣因子\n", + " parser.add_argument('--epsilon_start',default=0.95,type=float,help=\"initial value of epsilon\") # e-greedy策略中初始epsilon\n", + " parser.add_argument('--epsilon_end',default=0.01,type=float,help=\"final value of epsilon\") # e-greedy策略中的终止epsilon\n", + " parser.add_argument('--epsilon_decay',default=300,type=int,help=\"decay rate of epsilon\") # e-greedy策略中epsilon的衰减率\n", + " parser.add_argument('--lr',default=0.1,type=float,help=\"learning rate\")\n", + " parser.add_argument('--device',default='cpu',type=str,help=\"cpu or cuda\") \n", + " args = parser.parse_args([]) \n", + " return args\n", + "curr_time = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\") # 获取当前时间\n", + "\n", + "def smooth(data, weight=0.9): \n", + " '''用于平滑曲线，类似于Tensorboard中的smooth\n", + "\n", + " Args:\n", + " data (List):输入数据\n", + " weight (Float): 平滑权重，处于0-1之间，数值越高说明越平滑，一般取0.9\n", + "\n", + " Returns:\n", + " smoothed (List): 平滑后的数据\n", + " '''\n", + " last = data[0] # First value in the plot (first timestep)\n", + " smoothed = list()\n", + " for point in data:\n", + " smoothed_val = last * weight + (1 - weight) * point # 计算平滑值\n", + " smoothed.append(smoothed_val) \n", + " last = smoothed_val \n", + " return smoothed\n", + "\n", + "def plot_rewards(rewards,cfg, tag='train'):\n", + " sns.set()\n", + " plt.figure() # 创建一个图形实例，方便同时多画几个图\n", + " plt.title(\"learning curve on {} of {} for {}\".format(\n", + " cfg.device, cfg.algo_name, cfg.env_name))\n", + " plt.xlabel('epsiodes')\n", + " plt.plot(rewards, label='rewards')\n", + " plt.plot(smooth(rewards), label='smoothed')\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5、我准备好了！\n", + "\n", + "到现在我们真的可以像海绵宝宝那样大声说出来“我准备好了！“，跟着注释来看下效果吧～。" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "开始训练！\n", + "环境:CliffWalking-v0, 算法:Q-learning, 设备:cpu\n", + "回合：1/400，奖励：-1668.0，Epsilon：0.3771901652370099\n", + "回合：2/400，奖励：-2328.0，Epsilon：0.03210668110464856\n", + "回合：3/400，奖励：-152.0，Epsilon：0.02331928797825333\n", + "回合：4/400，奖励：-296.0，Epsilon：0.014965661602689185\n", + "回合：5/400，奖励：-168.0，Epsilon：0.012836430915462094\n", + "回合：6/400，奖励：-149.0，Epsilon：0.011726126490407173\n", + "回合：7/400，奖励：-274.0，Epsilon：0.010963239247691907\n", + "回合：8/400，奖励：-127.0，Epsilon：0.010630787152305933\n", + "回合：9/400，奖励：-356.0，Epsilon：0.010267816440118822\n", + "回合：10/400，奖励：-105.0，Epsilon：0.0101887270555826\n", + "回合：11/400，奖励：-162.0，Epsilon：0.01010998036181645\n", + "回合：12/400，奖励：-124.0，Epsilon：0.010072745604688937\n", + "回合：13/400，奖励：-125.0，Epsilon：0.010047956858279448\n", + "回合：14/400，奖励：-69.0，Epsilon：0.010038103335373512\n", + "回合：15/400，奖励：-146.0，Epsilon：0.010023421049147612\n", + "回合：16/400，奖励：-99.0，Epsilon：0.010016837948094095\n", + "回合：17/400，奖励：-102.0，Epsilon：0.010011984751749595\n", + "回合：18/400，奖励：-114.0，Epsilon：0.010008195909220538\n", + "回合：19/400，奖励：-95.0，Epsilon：0.010005971322860786\n", + "回合：20/400，奖励：-50.0，Epsilon：0.010005054615675078\n", + "回合：21/400，奖励：-179.0，Epsilon：0.010002783294099886\n", + "回合：22/400，奖励：-51.0，Epsilon：0.010002348167306314\n", + "回合：23/400，奖励：-53.0，Epsilon：0.010001967902958245\n", + "回合：24/400，奖励：-126.0，Epsilon：0.01000129300438042\n", + "回合：25/400，奖励：-105.0，Epsilon：0.010000911164786836\n", + "回合：26/400，奖励：-55.0，Epsilon：0.010000758536131584\n", + "回合：27/400，奖励：-112.0，Epsilon：0.010000522203364875\n", + "回合：28/400，奖励：-81.0，Epsilon：0.01000039863934062\n", + "回合：29/400，奖励：-187.0，Epsilon：0.010000297294659517\n", + "回合：30/400，奖励：-176.0，Epsilon：0.01000022999489198\n", + "回合：31/400，奖励：-71.0，Epsilon：0.010000181524464132\n", + "回合：32/400，奖励：-77.0，Epsilon：0.010000140432053464\n", + "回合：33/400，奖励：-82.0，Epsilon：0.010000106846201706\n", + "回合：34/400，奖励：-95.0，Epsilon：0.010000077845318887\n", + "回合：35/400，奖励：-53.0，Epsilon：0.010000065238977184\n", + "回合：36/400，奖励：-30.0，Epsilon：0.010000059030667672\n", + "回合：37/400，奖励：-122.0，Epsilon：0.010000039306520976\n", + "回合：38/400，奖励：-37.0，Epsilon：0.010000034745744355\n", + "回合：39/400，奖励：-100.0，Epsilon：0.01000002489641374\n", + "回合：40/400，奖励：-201.0，Epsilon：0.010000017720528442\n", + "回合：41/400，奖励：-62.0，Epsilon：0.010000014411941012\n", + "回合：42/400，奖励：-61.0，Epsilon：0.010000011760233133\n", + "回合：43/400，奖励：-57.0，Epsilon：0.010000009725232207\n", + "回合：44/400，奖励：-73.0，Epsilon：0.0100000076246806\n", + "回合：45/400，奖励：-39.0，Epsilon：0.010000006695197199\n", + "回合：46/400，奖励：-71.0，Epsilon：0.010000005284213373\n", + "回合：47/400，奖励：-77.0，Epsilon：0.010000004088005098\n", + "回合：48/400，奖励：-53.0，Epsilon：0.010000003425989836\n", + "回合：49/400，奖励：-88.0，Epsilon：0.010000002555012459\n", + "回合：50/400，奖励：-65.0，Epsilon：0.01000000205729175\n", + "回合：51/400，奖励：-41.0，Epsilon：0.010000001794495218\n", + "回合：52/400，奖励：-67.0，Epsilon：0.010000001435323749\n", + "回合：53/400，奖励：-38.0，Epsilon：0.010000001264559407\n", + "回合：54/400，奖励：-50.0，Epsilon：0.010000001070426428\n", + "回合：55/400，奖励：-35.0，Epsilon：0.010000000952552966\n", + "回合：56/400，奖励：-74.0，Epsilon：0.010000000744325952\n", + "回合：57/400，奖励：-75.0，Epsilon：0.010000000579681634\n", + "回合：58/400，奖励：-31.0，Epsilon：0.010000000522772152\n", + "回合：59/400，奖励：-38.0，Epsilon：0.010000000460576537\n", + "回合：60/400，奖励：-51.0，Epsilon：0.01000000038857222\n", + "回合：61/400，奖励：-64.0，Epsilon：0.010000000313922366\n", + "回合：62/400，奖励：-78.0，Epsilon：0.010000000242050338\n", + 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"回合：237/400，奖励：-20.0，Epsilon：0.010000000000000005\n", + "回合：238/400，奖励：-18.0，Epsilon：0.010000000000000005\n", + "回合：239/400，奖励：-14.0，Epsilon：0.010000000000000005\n", + "回合：240/400，奖励：-13.0，Epsilon：0.010000000000000005\n", + "回合：241/400，奖励：-28.0，Epsilon：0.010000000000000004\n", + "回合：242/400，奖励：-13.0，Epsilon：0.010000000000000004\n", + "回合：243/400，奖励：-23.0，Epsilon：0.010000000000000004\n", + "回合：244/400，奖励：-17.0，Epsilon：0.010000000000000004\n", + "回合：245/400，奖励：-14.0，Epsilon：0.010000000000000004\n", + "回合：246/400，奖励：-22.0，Epsilon：0.010000000000000004\n", + "回合：247/400，奖励：-15.0，Epsilon：0.010000000000000004\n", + "回合：248/400，奖励：-19.0，Epsilon：0.010000000000000004\n", + "回合：249/400，奖励：-17.0，Epsilon：0.010000000000000004\n", + "回合：250/400，奖励：-27.0，Epsilon：0.010000000000000002\n", + "回合：251/400，奖励：-21.0，Epsilon：0.010000000000000002\n", + "回合：252/400，奖励：-23.0，Epsilon：0.010000000000000002\n", + "回合：253/400，奖励：-15.0，Epsilon：0.010000000000000002\n", + "回合：254/400，奖励：-15.0，Epsilon：0.010000000000000002\n", + "回合：255/400，奖励：-13.0，Epsilon：0.010000000000000002\n", + "回合：256/400，奖励：-15.0，Epsilon：0.010000000000000002\n", + "回合：257/400，奖励：-13.0，Epsilon：0.010000000000000002\n", + "回合：258/400，奖励：-28.0，Epsilon：0.010000000000000002\n", + "回合：259/400，奖励：-13.0，Epsilon：0.010000000000000002\n", + "回合：260/400，奖励：-13.0，Epsilon：0.010000000000000002\n", + "回合：261/400，奖励：-23.0，Epsilon：0.010000000000000002\n", + "回合：262/400，奖励：-13.0，Epsilon：0.010000000000000002\n", + "回合：263/400，奖励：-24.0，Epsilon：0.010000000000000002\n", + "回合：264/400，奖励：-17.0，Epsilon：0.010000000000000002\n", + "回合：265/400，奖励：-19.0，Epsilon：0.010000000000000002\n", + "回合：266/400，奖励：-13.0，Epsilon：0.010000000000000002\n", + "回合：267/400，奖励：-25.0，Epsilon：0.010000000000000002\n", + "回合：268/400，奖励：-15.0，Epsilon：0.01\n", + "回合：269/400，奖励：-15.0，Epsilon：0.01\n", + "回合：270/400，奖励：-21.0，Epsilon：0.01\n", + "回合：271/400，奖励：-13.0，Epsilon：0.01\n", + "回合：272/400，奖励：-13.0，Epsilon：0.01\n", + "回合：273/400，奖励：-22.0，Epsilon：0.01\n", + "回合：274/400，奖励：-15.0，Epsilon：0.01\n", + "回合：275/400，奖励：-13.0，Epsilon：0.01\n", + "回合：276/400，奖励：-19.0，Epsilon：0.01\n", + "回合：277/400，奖励：-13.0，Epsilon：0.01\n", + "回合：278/400，奖励：-18.0，Epsilon：0.01\n", + "回合：279/400，奖励：-14.0，Epsilon：0.01\n", + "回合：280/400，奖励：-126.0，Epsilon：0.01\n", + "回合：281/400，奖励：-15.0，Epsilon：0.01\n", + "回合：282/400，奖励：-19.0，Epsilon：0.01\n", + "回合：283/400，奖励：-13.0，Epsilon：0.01\n", + "回合：284/400，奖励：-25.0，Epsilon：0.01\n", + "回合：285/400，奖励：-13.0，Epsilon：0.01\n", + "回合：286/400，奖励：-119.0，Epsilon：0.01\n", + "回合：287/400，奖励：-15.0，Epsilon：0.01\n", + "回合：288/400，奖励：-15.0，Epsilon：0.01\n", + "回合：289/400，奖励：-14.0，Epsilon：0.01\n", + "回合：290/400，奖励：-13.0，Epsilon：0.01\n", + "回合：291/400，奖励：-13.0，Epsilon：0.01\n", + "回合：292/400，奖励：-15.0，Epsilon：0.01\n", + "回合：293/400，奖励：-33.0，Epsilon：0.01\n", + "回合：294/400，奖励：-19.0，Epsilon：0.01\n", + "回合：295/400，奖励：-13.0，Epsilon：0.01\n", + "回合：296/400，奖励：-15.0，Epsilon：0.01\n", + "回合：297/400，奖励：-13.0，Epsilon：0.01\n", + "回合：298/400，奖励：-132.0，Epsilon：0.01\n", + "回合：299/400，奖励：-13.0，Epsilon：0.01\n", + "回合：300/400，奖励：-13.0，Epsilon：0.01\n", + "回合：301/400，奖励：-13.0，Epsilon：0.01\n", + "回合：302/400，奖励：-14.0，Epsilon：0.01\n", + "回合：303/400，奖励：-15.0，Epsilon：0.01\n", + "回合：304/400，奖励：-13.0，Epsilon：0.01\n", + "回合：305/400，奖励：-13.0，Epsilon：0.01\n", + "回合：306/400，奖励：-13.0，Epsilon：0.01\n", + "回合：307/400，奖励：-13.0，Epsilon：0.01\n", + "回合：308/400，奖励：-13.0，Epsilon：0.01\n", + "回合：309/400，奖励：-13.0，Epsilon：0.01\n", + "回合：310/400，奖励：-13.0，Epsilon：0.01\n", + "回合：311/400，奖励：-15.0，Epsilon：0.01\n", + "回合：312/400，奖励：-13.0，Epsilon：0.01\n", + "回合：313/400，奖励：-13.0，Epsilon：0.01\n", + "回合：314/400，奖励：-13.0，Epsilon：0.01\n", + "回合：315/400，奖励：-15.0，Epsilon：0.01\n", + "回合：316/400，奖励：-14.0，Epsilon：0.01\n", + "回合：317/400，奖励：-13.0，Epsilon：0.01\n", + "回合：318/400，奖励：-13.0，Epsilon：0.01\n", + "回合：319/400，奖励：-13.0，Epsilon：0.01\n", + "回合：320/400，奖励：-21.0，Epsilon：0.01\n", + "回合：321/400，奖励：-19.0，Epsilon：0.01\n", + "回合：322/400，奖励：-13.0，Epsilon：0.01\n", + "回合：323/400，奖励：-13.0，Epsilon：0.01\n", + "回合：324/400，奖励：-13.0，Epsilon：0.01\n", + "回合：325/400，奖励：-13.0，Epsilon：0.01\n", + "回合：326/400，奖励：-14.0，Epsilon：0.01\n", + "回合：327/400，奖励：-15.0，Epsilon：0.01\n", + "回合：328/400，奖励：-13.0，Epsilon：0.01\n", + "回合：329/400，奖励：-13.0，Epsilon：0.01\n", + "回合：330/400，奖励：-13.0，Epsilon：0.01\n", + "回合：331/400，奖励：-13.0，Epsilon：0.01\n", + "回合：332/400，奖励：-13.0，Epsilon：0.01\n", + "回合：333/400，奖励：-14.0，Epsilon：0.01\n", + "回合：334/400，奖励：-13.0，Epsilon：0.01\n", + "回合：335/400，奖励：-113.0，Epsilon：0.01\n", + "回合：336/400，奖励：-13.0，Epsilon：0.01\n", + "回合：337/400，奖励：-13.0，Epsilon：0.01\n", + "回合：338/400，奖励：-13.0，Epsilon：0.01\n", + "回合：339/400，奖励：-13.0，Epsilon：0.01\n", + "回合：340/400，奖励：-13.0，Epsilon：0.01\n", + "回合：341/400，奖励：-15.0，Epsilon：0.01\n", + "回合：342/400，奖励：-23.0，Epsilon：0.01\n", + "回合：343/400，奖励：-13.0，Epsilon：0.01\n", + "回合：344/400，奖励：-13.0，Epsilon：0.01\n", + "回合：345/400，奖励：-13.0，Epsilon：0.01\n", + "回合：346/400，奖励：-13.0，Epsilon：0.01\n", + "回合：347/400，奖励：-13.0，Epsilon：0.01\n", + "回合：348/400，奖励：-13.0，Epsilon：0.01\n", + "回合：349/400，奖励：-13.0，Epsilon：0.01\n", + "回合：350/400，奖励：-13.0，Epsilon：0.01\n", + "回合：351/400，奖励：-13.0，Epsilon：0.01\n", + "回合：352/400，奖励：-13.0，Epsilon：0.01\n", + "回合：353/400，奖励：-13.0，Epsilon：0.01\n", + "回合：354/400，奖励：-13.0，Epsilon：0.01\n", + "回合：355/400，奖励：-13.0，Epsilon：0.01\n", + "回合：356/400，奖励：-13.0，Epsilon：0.01\n", + "回合：357/400，奖励：-13.0，Epsilon：0.01\n", + "回合：358/400，奖励：-13.0，Epsilon：0.01\n", + "回合：359/400，奖励：-13.0，Epsilon：0.01\n", + "回合：360/400，奖励：-13.0，Epsilon：0.01\n", + "回合：361/400，奖励：-13.0，Epsilon：0.01\n", + "回合：362/400，奖励：-13.0，Epsilon：0.01\n", + "回合：363/400，奖励：-13.0，Epsilon：0.01\n", + "回合：364/400，奖励：-13.0，Epsilon：0.01\n", + "回合：365/400，奖励：-13.0，Epsilon：0.01\n", + "回合：366/400，奖励：-13.0，Epsilon：0.01\n", + "回合：367/400，奖励：-13.0，Epsilon：0.01\n", + "回合：368/400，奖励：-13.0，Epsilon：0.01\n", + "回合：369/400，奖励：-13.0，Epsilon：0.01\n", + "回合：370/400，奖励：-13.0，Epsilon：0.01\n", + "回合：371/400，奖励：-13.0，Epsilon：0.01\n", + "回合：372/400，奖励：-14.0，Epsilon：0.01\n", + "回合：373/400，奖励：-13.0，Epsilon：0.01\n", + "回合：374/400，奖励：-15.0，Epsilon：0.01\n", + "回合：375/400，奖励：-13.0，Epsilon：0.01\n", + "回合：376/400，奖励：-13.0，Epsilon：0.01\n", + "回合：377/400，奖励：-13.0，Epsilon：0.01\n", + "回合：378/400，奖励：-13.0，Epsilon：0.01\n", + "回合：379/400，奖励：-13.0，Epsilon：0.01\n", + "回合：380/400，奖励：-117.0，Epsilon：0.01\n", + "回合：381/400，奖励：-13.0，Epsilon：0.01\n", + "回合：382/400，奖励：-13.0，Epsilon：0.01\n", + "回合：383/400，奖励：-13.0，Epsilon：0.01\n", + "回合：384/400，奖励：-13.0，Epsilon：0.01\n", + "回合：385/400，奖励：-13.0，Epsilon：0.01\n", + "回合：386/400，奖励：-13.0，Epsilon：0.01\n", + "回合：387/400，奖励：-13.0，Epsilon：0.01\n", + "回合：388/400，奖励：-13.0，Epsilon：0.01\n", + "回合：389/400，奖励：-13.0，Epsilon：0.01\n", + "回合：390/400，奖励：-13.0，Epsilon：0.01\n", + "回合：391/400，奖励：-13.0，Epsilon：0.01\n", + "回合：392/400，奖励：-13.0，Epsilon：0.01\n", + "回合：393/400，奖励：-13.0，Epsilon：0.01\n", + "回合：394/400，奖励：-13.0，Epsilon：0.01\n", + "回合：395/400，奖励：-13.0，Epsilon：0.01\n", + "回合：396/400，奖励：-13.0，Epsilon：0.01\n", + "回合：397/400，奖励：-13.0，Epsilon：0.01\n", + "回合：398/400，奖励：-15.0，Epsilon：0.01\n", + "回合：399/400，奖励：-13.0，Epsilon：0.01\n", + "回合：400/400，奖励：-13.0，Epsilon：0.01\n", + "完成训练！\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "

" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "开始测试！\n", + "环境：CliffWalking-v0, 算法：Q-learning, 设备：cpu\n", + "回合数：1/20, 奖励：-13.0\n", + "回合数：2/20, 奖励：-13.0\n", + "回合数：3/20, 奖励：-13.0\n", + "回合数：4/20, 奖励：-13.0\n", + "回合数：5/20, 奖励：-13.0\n", + "回合数：6/20, 奖励：-13.0\n", + "回合数：7/20, 奖励：-13.0\n", + "回合数：8/20, 奖励：-13.0\n", + "回合数：9/20, 奖励：-13.0\n", + "回合数：10/20, 奖励：-13.0\n", + "回合数：11/20, 奖励：-13.0\n", + "回合数：12/20, 奖励：-13.0\n", + "回合数：13/20, 奖励：-13.0\n", + "回合数：14/20, 奖励：-13.0\n", + "回合数：15/20, 奖励：-13.0\n", + "回合数：16/20, 奖励：-13.0\n", + "回合数：17/20, 奖励：-13.0\n", + "回合数：18/20, 奖励：-13.0\n", + "回合数：19/20, 奖励：-13.0\n", + "回合数：20/20, 奖励：-13.0\n", + "完成测试！\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 获取参数\n", + "cfg = get_args() \n", + "# 训练\n", + "env, agent = env_agent_config(cfg)\n", + "res_dic = train(cfg, env, agent)\n", + " \n", + "plot_rewards(res_dic['rewards'], cfg, tag=\"train\") \n", + "# 测试\n", + "res_dic = test(cfg, env, agent)\n", + "plot_rewards(res_dic['rewards'], cfg, tag=\"test\") # 画出结果" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.7.13 ('easyrl')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "8994a120d39b6e6a2ecc94b4007f5314b68aa69fc88a7f00edf21be39b41f49c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/projects/notebooks/2.Sarsa.ipynb b/projects/notebooks/2.Sarsa.ipynb new file mode 100644 index 0000000..493cb59 --- /dev/null +++ b/projects/notebooks/2.Sarsa.ipynb @@ -0,0 +1,896 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1、定义算法\n", + "\n", + "在阅读该教程之前，请先阅读Q learning教程。Sarsa算法跟Q learning算法基本模式相同，但是根本的区别在于，Sarsa是先做出动作然后拿这个做的动作去更新，而Q learning是假定下一步最大奖励对应的动作拿去更新，然后再使用$\\varepsilon$-greedy策略，也就是说Sarsa是on-policy的，而Q learning是off-policy的。如下方代码所示，只有在更新的地方Sarsa与Q learning有着细微的区别。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from collections import defaultdict\n", + "import torch\n", + "import math\n", + "class Sarsa(object):\n", + " def __init__(self,\n", + " n_actions,cfg):\n", + " self.n_actions = n_actions \n", + " self.lr = cfg.lr \n", + " self.gamma = cfg.gamma \n", + " self.sample_count = 0 \n", + " self.epsilon_start = cfg.epsilon_start\n", + " self.epsilon_end = cfg.epsilon_end\n", + " self.epsilon_decay = cfg.epsilon_decay \n", + " self.Q = defaultdict(lambda: np.zeros(n_actions)) # Q table\n", + " def sample(self, state):\n", + " self.sample_count += 1\n", + " self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \\\n", + " math.exp(-1. * self.sample_count / self.epsilon_decay) # The probability to select a random action, is is log decayed\n", + " best_action = np.argmax(self.Q[state])\n", + " action_probs = np.ones(self.n_actions, dtype=float) * self.epsilon / self.n_actions\n", + " action_probs[best_action] += (1.0 - self.epsilon)\n", + " action = np.random.choice(np.arange(len(action_probs)), p=action_probs) \n", + " return action\n", + " def predict(self,state):\n", + " return np.argmax(self.Q[state])\n", + " def update(self, state, action, reward, next_state, next_action,done):\n", + " Q_predict = self.Q[state][action]\n", + " if done:\n", + " Q_target = reward # 终止状态\n", + " else:\n", + " Q_target = reward + self.gamma * self.Q[next_state][next_action] # 与Q learning不同，Sarsa是拿下一步动作对应的Q值去更新\n", + " self.Q[state][action] += self.lr * (Q_target - Q_predict) \n", + " def save(self,path):\n", + " '''把 Q表格 的数据保存到文件中\n", + " '''\n", + " import dill\n", + " torch.save(\n", + " obj=self.Q,\n", + " f=path+\"sarsa_model.pkl\",\n", + " pickle_module=dill\n", + " )\n", + " def load(self, path):\n", + " '''从文件中读取数据到 Q表格\n", + " '''\n", + " import dill\n", + " self.Q =torch.load(f=path+'sarsa_model.pkl',pickle_module=dill)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2、定义训练\n", + "\n", + "同样地，跟Q learning差别也不大" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def train(cfg,env,agent):\n", + " print('开始训练！')\n", + " print(f'环境:{cfg.env_name}, 算法:{cfg.algo_name}, 设备:{cfg.device}')\n", + " rewards = [] # 记录奖励\n", + " for i_ep in range(cfg.train_eps):\n", + " ep_reward = 0 # 记录每个回合的奖励\n", + " state = env.reset() # 重置环境,即开始新的回合\n", + " action = agent.sample(state)\n", + " while True:\n", + " action = agent.sample(state) # 根据算法采样一个动作\n", + " next_state, reward, done, _ = env.step(action) # 与环境进行一次动作交互\n", + " next_action = agent.sample(next_state)\n", + " agent.update(state, action, reward, next_state, next_action,done) # 算法更新\n", + " state = next_state # 更新状态\n", + " action = next_action\n", + " ep_reward += reward\n", + " if done:\n", + " break\n", + " rewards.append(ep_reward)\n", + " print(f\"回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward:.1f}，Epsilon：{agent.epsilon}\")\n", + " print('完成训练！')\n", + " return {\"rewards\":rewards}\n", + " \n", + "def test(cfg,env,agent):\n", + " print('开始测试！')\n", + " print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')\n", + " rewards = [] # 记录所有回合的奖励\n", + " for i_ep in range(cfg.test_eps):\n", + " ep_reward = 0 # 记录每个episode的reward\n", + " state = env.reset() # 重置环境, 重新开一局（即开始新的一个回合）\n", + " while True:\n", + " action = agent.predict(state) # 根据算法选择一个动作\n", + " next_state, reward, done, _ = env.step(action) # 与环境进行一个交互\n", + " state = next_state # 更新状态\n", + " ep_reward += reward\n", + " if done:\n", + " break\n", + " rewards.append(ep_reward)\n", + " print(f\"回合数：{i_ep+1}/{cfg.test_eps}, 奖励：{ep_reward:.1f}\")\n", + " print('完成测试！')\n", + " return {\"rewards\":rewards}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3、定义环境\n", + "为了具体看看Q learning和Sarsa的不同，笔者决定跟Q learning使用相同的环境\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import gym\n", + "import turtle\n", + "import numpy as np\n", + "\n", + "# turtle tutorial : https://docs.python.org/3.3/library/turtle.html\n", + "\n", + "def GridWorld(gridmap=None, is_slippery=False):\n", + " if gridmap is None:\n", + " gridmap = ['SFFF', 'FHFH', 'FFFH', 'HFFG']\n", + " env = gym.make(\"FrozenLake-v0\", desc=gridmap, is_slippery=False)\n", + " env = FrozenLakeWapper(env)\n", + " return env\n", + "\n", + "\n", + "class FrozenLakeWapper(gym.Wrapper):\n", + " def __init__(self, env):\n", + " gym.Wrapper.__init__(self, env)\n", + " self.max_y = env.desc.shape[0]\n", + " self.max_x = env.desc.shape[1]\n", + " self.t = None\n", + " self.unit = 50\n", + "\n", + " def draw_box(self, x, y, fillcolor='', line_color='gray'):\n", + " self.t.up()\n", + " self.t.goto(x * self.unit, y * self.unit)\n", + " self.t.color(line_color)\n", + " self.t.fillcolor(fillcolor)\n", + " self.t.setheading(90)\n", + " self.t.down()\n", + " self.t.begin_fill()\n", + " for _ in range(4):\n", + " self.t.forward(self.unit)\n", + " self.t.right(90)\n", + " self.t.end_fill()\n", + "\n", + " def move_player(self, x, y):\n", + " self.t.up()\n", + " self.t.setheading(90)\n", + " self.t.fillcolor('red')\n", + " self.t.goto((x + 0.5) * self.unit, (y + 0.5) * self.unit)\n", + "\n", + " def render(self):\n", + " if self.t == None:\n", + " self.t = turtle.Turtle()\n", + " self.wn = turtle.Screen()\n", + " self.wn.setup(self.unit * self.max_x + 100,\n", + " self.unit * self.max_y + 100)\n", + " self.wn.setworldcoordinates(0, 0, self.unit * self.max_x,\n", + " self.unit * self.max_y)\n", + " self.t.shape('circle')\n", + " self.t.width(2)\n", + " self.t.speed(0)\n", + " self.t.color('gray')\n", + " for i in range(self.desc.shape[0]):\n", + " for j in range(self.desc.shape[1]):\n", + " x = j\n", + " y = self.max_y - 1 - i\n", + " if self.desc[i][j] == b'S': # Start\n", + " self.draw_box(x, y, 'white')\n", + " elif self.desc[i][j] == b'F': # Frozen ice\n", + " self.draw_box(x, y, 'white')\n", + " elif self.desc[i][j] == b'G': # Goal\n", + " self.draw_box(x, y, 'yellow')\n", + " elif self.desc[i][j] == b'H': # Hole\n", + " self.draw_box(x, y, 'black')\n", + " else:\n", + " self.draw_box(x, y, 'white')\n", + " self.t.shape('turtle')\n", + "\n", + " x_pos = self.s % self.max_x\n", + " y_pos = self.max_y - 1 - int(self.s / self.max_x)\n", + " self.move_player(x_pos, y_pos)\n", + "\n", + "\n", + "class CliffWalkingWapper(gym.Wrapper):\n", + " def __init__(self, env):\n", + " gym.Wrapper.__init__(self, env)\n", + " self.t = None\n", + " self.unit = 50\n", + " self.max_x = 12\n", + " self.max_y = 4\n", + "\n", + " def draw_x_line(self, y, x0, x1, color='gray'):\n", + " assert x1 > x0\n", + " self.t.color(color)\n", + " self.t.setheading(0)\n", + " self.t.up()\n", + " self.t.goto(x0, y)\n", + " self.t.down()\n", + " self.t.forward(x1 - x0)\n", + "\n", + " def draw_y_line(self, x, y0, y1, color='gray'):\n", + " assert y1 > y0\n", + " self.t.color(color)\n", + " self.t.setheading(90)\n", + " self.t.up()\n", + " self.t.goto(x, y0)\n", + " self.t.down()\n", + " self.t.forward(y1 - y0)\n", + "\n", + " def draw_box(self, x, y, fillcolor='', line_color='gray'):\n", + " self.t.up()\n", + " self.t.goto(x * self.unit, y * self.unit)\n", + " self.t.color(line_color)\n", + " self.t.fillcolor(fillcolor)\n", + " self.t.setheading(90)\n", + " self.t.down()\n", + " self.t.begin_fill()\n", + " for i in range(4):\n", + " self.t.forward(self.unit)\n", + " self.t.right(90)\n", + " self.t.end_fill()\n", + "\n", + " def move_player(self, x, y):\n", + " self.t.up()\n", + " self.t.setheading(90)\n", + " self.t.fillcolor('red')\n", + " self.t.goto((x + 0.5) * self.unit, (y + 0.5) * self.unit)\n", + "\n", + " def render(self):\n", + " if self.t == None:\n", + " self.t = turtle.Turtle()\n", + " self.wn = turtle.Screen()\n", + " self.wn.setup(self.unit * self.max_x + 100,\n", + " self.unit * self.max_y + 100)\n", + " self.wn.setworldcoordinates(0, 0, self.unit * self.max_x,\n", + " self.unit * self.max_y)\n", + " self.t.shape('circle')\n", + " self.t.width(2)\n", + " self.t.speed(0)\n", + " self.t.color('gray')\n", + " for _ in range(2):\n", + " self.t.forward(self.max_x * self.unit)\n", + " self.t.left(90)\n", + " self.t.forward(self.max_y * self.unit)\n", + " self.t.left(90)\n", + " for i in range(1, self.max_y):\n", + " self.draw_x_line(\n", + " y=i * self.unit, x0=0, x1=self.max_x * self.unit)\n", + " for i in range(1, self.max_x):\n", + " self.draw_y_line(\n", + " x=i * self.unit, y0=0, y1=self.max_y * self.unit)\n", + "\n", + " for i in range(1, self.max_x - 1):\n", + " self.draw_box(i, 0, 'black')\n", + " self.draw_box(self.max_x - 1, 0, 'yellow')\n", + " self.t.shape('turtle')\n", + "\n", + " x_pos = self.s % self.max_x\n", + " y_pos = self.max_y - 1 - int(self.s / self.max_x)\n", + " self.move_player(x_pos, y_pos)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def env_agent_config(cfg,seed=1):\n", + " '''创建环境和智能体\n", + " Args:\n", + " cfg ([type]): [description]\n", + " seed (int, optional): 随机种子. Defaults to 1.\n", + " Returns:\n", + " env [type]: 环境\n", + " agent : 智能体\n", + " ''' \n", + " env = gym.make(cfg.env_name) \n", + " env = CliffWalkingWapper(env)\n", + " env.seed(seed) # 设置随机种子\n", + " n_states = env.observation_space.n # 状态维度\n", + " n_actions = env.action_space.n # 动作维度\n", + " print(f\"状态数：{n_states}，动作数：{n_actions}\")\n", + " agent = Sarsa(n_actions,cfg)\n", + " return env,agent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4、设置参数\n", + "同样的参数也是一样" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import datetime\n", + "import argparse\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "def get_args():\n", + " \"\"\" \n", + " \"\"\"\n", + " curr_time = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\") # 获取当前时间\n", + " parser = argparse.ArgumentParser(description=\"hyperparameters\") \n", + " parser.add_argument('--algo_name',default='Sarsa',type=str,help=\"name of algorithm\")\n", + " parser.add_argument('--env_name',default='CliffWalking-v0',type=str,help=\"name of environment\")\n", + " parser.add_argument('--train_eps',default=400,type=int,help=\"episodes of training\") # 训练的回合数\n", + " parser.add_argument('--test_eps',default=20,type=int,help=\"episodes of testing\") # 测试的回合数\n", + " parser.add_argument('--gamma',default=0.90,type=float,help=\"discounted factor\") # 折扣因子\n", + " parser.add_argument('--epsilon_start',default=0.95,type=float,help=\"initial value of epsilon\") # e-greedy策略中初始epsilon\n", + " parser.add_argument('--epsilon_end',default=0.01,type=float,help=\"final value of epsilon\") # e-greedy策略中的终止epsilon\n", + " parser.add_argument('--epsilon_decay',default=300,type=int,help=\"decay rate of epsilon\") # e-greedy策略中epsilon的衰减率\n", + " parser.add_argument('--lr',default=0.1,type=float,help=\"learning rate\")\n", + " parser.add_argument('--device',default='cpu',type=str,help=\"cpu or cuda\") \n", + " args = parser.parse_args([]) \n", + " return args\n", + "\n", + "def smooth(data, weight=0.9): \n", + " '''用于平滑曲线，类似于Tensorboard中的smooth\n", + "\n", + " Args:\n", + " data (List):输入数据\n", + " weight (Float): 平滑权重，处于0-1之间，数值越高说明越平滑，一般取0.9\n", + "\n", + " Returns:\n", + " smoothed (List): 平滑后的数据\n", + " '''\n", + " last = data[0] # First value in the plot (first timestep)\n", + " smoothed = list()\n", + " for point in data:\n", + " smoothed_val = last * weight + (1 - weight) * point # 计算平滑值\n", + " smoothed.append(smoothed_val) \n", + " last = smoothed_val \n", + " return smoothed\n", + "\n", + "def plot_rewards(rewards,cfg, tag='train'):\n", + " sns.set()\n", + " plt.figure() # 创建一个图形实例，方便同时多画几个图\n", + " plt.title(f\"{tag}ing curve on {cfg.device} of {cfg.algo_name} for {cfg.env_name}\")\n", + " plt.xlabel('epsiodes')\n", + " plt.plot(rewards, label='rewards')\n", + " plt.plot(smooth(rewards), label='smoothed')\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5、我准备好了！\n", + "仔细看，会发现Sarsa收敛得快一些，但是收敛之会低些，Q learning会相反，至于为什么请同学们自行思考哟~" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "状态数：48，动作数：4\n", + "开始训练！\n", + "环境:CliffWalking-v0, 算法:Sarsa, 设备:cpu\n", + "回合：1/400，奖励：-1524.0，Epsilon：0.2029722781251147\n", + "回合：2/400，奖励：-1294.0，Epsilon：0.011808588201828951\n", + "回合：3/400，奖励：-192.0，Epsilon：0.01050118158853445\n", + "回合：4/400，奖励：-346.0，Epsilon：0.010049747911736582\n", + "回合：5/400，奖励：-252.0，Epsilon：0.010009240861841986\n", + "回合：6/400，奖励：-168.0，Epsilon：0.010003005072880926\n", + "回合：7/400，奖励：-393.0，Epsilon：0.01000042188120369\n", + "回合：8/400，奖励：-169.0，Epsilon：0.010000136281659052\n", + "回合：9/400，奖励：-97.0，Epsilon：0.010000071145264558\n", + "回合：10/400，奖励：-134.0，Epsilon：0.010000029022085234\n", + "回合：11/400，奖励：-124.0，Epsilon：0.010000012655059554\n", + "回合：12/400，奖励：-74.0，Epsilon：0.010000007701309915\n", + "回合：13/400，奖励：-135.0，Epsilon：0.010000003120699265\n", + "回合：14/400，奖励：-84.0，Epsilon：0.010000001776639691\n", + "回合：15/400，奖励：-101.0，Epsilon：0.010000000903081117\n", + "回合：16/400，奖励：-111.0，Epsilon：0.010000000429438717\n", + "回合：17/400，奖励：-114.0，Epsilon：0.010000000200165738\n", + "回合：18/400，奖励：-114.0，Epsilon：0.010000000093299278\n", + "回合：19/400，奖励：-82.0，Epsilon：0.010000000053829002\n", + "回合：20/400，奖励：-85.0，Epsilon：0.01000000003044167\n", + "回合：21/400，奖励：-108.0，Epsilon：0.010000000014768242\n", + "回合：22/400，奖励：-66.0，Epsilon：0.010000000009479634\n", + "回合：23/400，奖励：-74.0，Epsilon：0.010000000005768887\n", + "回合：24/400，奖励：-114.0，Epsilon：0.010000000002688936\n", + "回合：25/400，奖励：-98.0，Epsilon：0.010000000001394421\n", + "回合：26/400，奖励：-94.0，Epsilon：0.010000000000742658\n", + "回合：27/400，奖励：-58.0，Epsilon：0.010000000000502822\n", + "回合：28/400，奖励：-100.0，Epsilon：0.010000000000257298\n", + "回合：29/400，奖励：-208.0，Epsilon：0.010000000000123995\n", + "回合：30/400，奖励：-184.0，Epsilon：0.010000000000070121\n", + "回合：31/400，奖励：-62.0，Epsilon：0.010000000000046227\n", + "回合：32/400，奖励：-117.0，Epsilon：0.01000000000002112\n", + "回合：33/400，奖励：-47.0，Epsilon：0.010000000000015387\n", + "回合：34/400，奖励：-54.0，Epsilon：0.0100000000000107\n", + "回合：35/400，奖励：-120.0，Epsilon：0.010000000000004792\n", + "回合：36/400，奖励：-75.0，Epsilon：0.010000000000002897\n", + "回合：37/400，奖励：-62.0，Epsilon：0.01000000000000191\n", + "回合：38/400，奖励：-70.0，Epsilon：0.010000000000001194\n", + "回合：39/400，奖励：-67.0，Epsilon：0.010000000000000762\n", + "回合：40/400，奖励：-87.0，Epsilon：0.010000000000000425\n", + "回合：41/400，奖励：-92.0，Epsilon：0.01000000000000023\n", + "回合：42/400，奖励：-79.0，Epsilon：0.010000000000000136\n", + "回合：43/400，奖励：-49.0，Epsilon：0.010000000000000097\n", + "回合：44/400，奖励：-103.0，Epsilon：0.010000000000000049\n", + "回合：45/400，奖励：-40.0，Epsilon：0.010000000000000037\n", + "回合：46/400，奖励：-214.0，Epsilon：0.010000000000000018\n", + "回合：47/400，奖励：-83.0，Epsilon：0.01000000000000001\n", + "回合：48/400，奖励：-62.0，Epsilon：0.010000000000000007\n", + "回合：49/400，奖励：-37.0，Epsilon：0.010000000000000005\n", + "回合：50/400，奖励：-73.0，Epsilon：0.010000000000000004\n", + "回合：51/400，奖励：-66.0，Epsilon：0.010000000000000002\n", + "回合：52/400，奖励：-48.0，Epsilon：0.010000000000000002\n", + "回合：53/400，奖励：-96.0，Epsilon：0.01\n", + "回合：54/400，奖励：-189.0，Epsilon：0.01\n", + "回合：55/400，奖励：-42.0，Epsilon：0.01\n", + "回合：56/400，奖励：-46.0，Epsilon：0.01\n", + "回合：57/400，奖励：-85.0，Epsilon：0.01\n", + "回合：58/400，奖励：-52.0，Epsilon：0.01\n", + "回合：59/400，奖励：-86.0，Epsilon：0.01\n", + "回合：60/400，奖励：-41.0，Epsilon：0.01\n", + "回合：61/400，奖励：-51.0，Epsilon：0.01\n", + "回合：62/400，奖励：-59.0，Epsilon：0.01\n", + "回合：63/400，奖励：-145.0，Epsilon：0.01\n", + "回合：64/400，奖励：-76.0，Epsilon：0.01\n", + "回合：65/400，奖励：-43.0，Epsilon：0.01\n", + "回合：66/400，奖励：-49.0，Epsilon：0.01\n", + "回合：67/400，奖励：-36.0，Epsilon：0.01\n", + "回合：68/400，奖励：-41.0，Epsilon：0.01\n", + "回合：69/400，奖励：-69.0，Epsilon：0.01\n", + 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+ "回合：245/400，奖励：-29.0，Epsilon：0.01\n", + "回合：246/400，奖励：-26.0，Epsilon：0.01\n", + "回合：247/400，奖励：-19.0，Epsilon：0.01\n", + "回合：248/400，奖励：-21.0，Epsilon：0.01\n", + "回合：249/400，奖励：-17.0，Epsilon：0.01\n", + "回合：250/400，奖励：-17.0，Epsilon：0.01\n", + "回合：251/400，奖励：-15.0，Epsilon：0.01\n", + "回合：252/400，奖励：-20.0，Epsilon：0.01\n", + "回合：253/400，奖励：-23.0，Epsilon：0.01\n", + "回合：254/400，奖励：-19.0，Epsilon：0.01\n", + "回合：255/400，奖励：-21.0，Epsilon：0.01\n", + "回合：256/400，奖励：-19.0，Epsilon：0.01\n", + "回合：257/400，奖励：-17.0，Epsilon：0.01\n", + "回合：258/400，奖励：-17.0，Epsilon：0.01\n", + "回合：259/400，奖励：-15.0，Epsilon：0.01\n", + "回合：260/400，奖励：-21.0，Epsilon：0.01\n", + "回合：261/400，奖励：-17.0，Epsilon：0.01\n", + "回合：262/400，奖励：-19.0，Epsilon：0.01\n", + "回合：263/400，奖励：-19.0，Epsilon：0.01\n", + "回合：264/400，奖励：-15.0，Epsilon：0.01\n", + "回合：265/400，奖励：-19.0，Epsilon：0.01\n", + "回合：266/400，奖励：-17.0，Epsilon：0.01\n", + "回合：267/400，奖励：-15.0，Epsilon：0.01\n", + "回合：268/400，奖励：-19.0，Epsilon：0.01\n", + "回合：269/400，奖励：-27.0，Epsilon：0.01\n", + "回合：270/400，奖励：-15.0，Epsilon：0.01\n", + "回合：271/400，奖励：-17.0，Epsilon：0.01\n", + "回合：272/400，奖励：-17.0，Epsilon：0.01\n", + "回合：273/400，奖励：-25.0，Epsilon：0.01\n", + "回合：274/400，奖励：-19.0，Epsilon：0.01\n", + "回合：275/400，奖励：-22.0，Epsilon：0.01\n", + "回合：276/400，奖励：-23.0，Epsilon：0.01\n", + "回合：277/400，奖励：-18.0，Epsilon：0.01\n", + "回合：278/400，奖励：-23.0，Epsilon：0.01\n", + "回合：279/400，奖励：-21.0，Epsilon：0.01\n", + "回合：280/400，奖励：-21.0，Epsilon：0.01\n", + "回合：281/400，奖励：-21.0，Epsilon：0.01\n", + "回合：282/400，奖励：-19.0，Epsilon：0.01\n", + "回合：283/400，奖励：-18.0，Epsilon：0.01\n", + "回合：284/400，奖励：-15.0，Epsilon：0.01\n", + "回合：285/400，奖励：-19.0，Epsilon：0.01\n", + "回合：286/400，奖励：-19.0，Epsilon：0.01\n", + "回合：287/400，奖励：-21.0，Epsilon：0.01\n", + "回合：288/400，奖励：-15.0，Epsilon：0.01\n", + "回合：289/400，奖励：-32.0，Epsilon：0.01\n", + "回合：290/400，奖励：-18.0，Epsilon：0.01\n", + "回合：291/400，奖励：-17.0，Epsilon：0.01\n", + "回合：292/400，奖励：-15.0，Epsilon：0.01\n", + "回合：293/400，奖励：-24.0，Epsilon：0.01\n", + "回合：294/400，奖励：-22.0，Epsilon：0.01\n", + "回合：295/400，奖励：-31.0，Epsilon：0.01\n", + "回合：296/400，奖励：-17.0，Epsilon：0.01\n", + "回合：297/400，奖励：-19.0，Epsilon：0.01\n", + "回合：298/400，奖励：-19.0，Epsilon：0.01\n", + "回合：299/400，奖励：-20.0，Epsilon：0.01\n", + "回合：300/400，奖励：-21.0，Epsilon：0.01\n", + "回合：301/400，奖励：-26.0，Epsilon：0.01\n", + "回合：302/400，奖励：-20.0，Epsilon：0.01\n", + "回合：303/400，奖励：-16.0，Epsilon：0.01\n", + "回合：304/400，奖励：-20.0，Epsilon：0.01\n", + "回合：305/400，奖励：-21.0，Epsilon：0.01\n", + "回合：306/400，奖励：-16.0，Epsilon：0.01\n", + "回合：307/400，奖励：-19.0，Epsilon：0.01\n", + "回合：308/400，奖励：-24.0，Epsilon：0.01\n", + "回合：309/400，奖励：-20.0，Epsilon：0.01\n", + "回合：310/400，奖励：-17.0，Epsilon：0.01\n", + "回合：311/400，奖励：-16.0，Epsilon：0.01\n", + "回合：312/400，奖励：-25.0，Epsilon：0.01\n", + "回合：313/400，奖励：-16.0，Epsilon：0.01\n", + "回合：314/400，奖励：-19.0，Epsilon：0.01\n", + "回合：315/400，奖励：-19.0，Epsilon：0.01\n", + "回合：316/400，奖励：-27.0，Epsilon：0.01\n", + "回合：317/400，奖励：-15.0，Epsilon：0.01\n", + "回合：318/400，奖励：-15.0，Epsilon：0.01\n", + "回合：319/400，奖励：-15.0，Epsilon：0.01\n", + "回合：320/400，奖励：-19.0，Epsilon：0.01\n", + "回合：321/400，奖励：-23.0，Epsilon：0.01\n", + "回合：322/400，奖励：-24.0，Epsilon：0.01\n", + "回合：323/400，奖励：-15.0，Epsilon：0.01\n", + "回合：324/400，奖励：-20.0，Epsilon：0.01\n", + "回合：325/400，奖励：-18.0，Epsilon：0.01\n", + "回合：326/400，奖励：-19.0，Epsilon：0.01\n", + "回合：327/400，奖励：-19.0，Epsilon：0.01\n", + "回合：328/400，奖励：-26.0，Epsilon：0.01\n", + "回合：329/400，奖励：-16.0，Epsilon：0.01\n", + "回合：330/400，奖励：-18.0，Epsilon：0.01\n", + "回合：331/400，奖励：-15.0，Epsilon：0.01\n", + "回合：332/400，奖励：-15.0，Epsilon：0.01\n", + "回合：333/400，奖励：-17.0，Epsilon：0.01\n", + "回合：334/400，奖励：-17.0，Epsilon：0.01\n", + "回合：335/400，奖励：-16.0，Epsilon：0.01\n", + "回合：336/400，奖励：-24.0，Epsilon：0.01\n", + "回合：337/400，奖励：-15.0，Epsilon：0.01\n", + "回合：338/400，奖励：-18.0，Epsilon：0.01\n", + "回合：339/400，奖励：-16.0，Epsilon：0.01\n", + "回合：340/400，奖励：-15.0，Epsilon：0.01\n", + "回合：341/400，奖励：-18.0，Epsilon：0.01\n", + "回合：342/400，奖励：-15.0，Epsilon：0.01\n", + "回合：343/400，奖励：-20.0，Epsilon：0.01\n", + "回合：344/400，奖励：-18.0，Epsilon：0.01\n", + "回合：345/400，奖励：-17.0，Epsilon：0.01\n", + "回合：346/400，奖励：-19.0，Epsilon：0.01\n", + "回合：347/400，奖励：-15.0，Epsilon：0.01\n", + "回合：348/400，奖励：-15.0，Epsilon：0.01\n", + "回合：349/400，奖励：-15.0，Epsilon：0.01\n", + "回合：350/400，奖励：-18.0，Epsilon：0.01\n", + "回合：351/400，奖励：-16.0，Epsilon：0.01\n", + "回合：352/400，奖励：-16.0，Epsilon：0.01\n", + "回合：353/400，奖励：-15.0，Epsilon：0.01\n", + "回合：354/400，奖励：-20.0，Epsilon：0.01\n", + "回合：355/400，奖励：-15.0，Epsilon：0.01\n", + "回合：356/400，奖励：-17.0，Epsilon：0.01\n", + "回合：357/400，奖励：-15.0，Epsilon：0.01\n", + "回合：358/400，奖励：-17.0，Epsilon：0.01\n", + "回合：359/400，奖励：-15.0，Epsilon：0.01\n", + "回合：360/400，奖励：-16.0，Epsilon：0.01\n", + "回合：361/400，奖励：-15.0，Epsilon：0.01\n", + "回合：362/400，奖励：-18.0，Epsilon：0.01\n", + "回合：363/400，奖励：-17.0，Epsilon：0.01\n", + "回合：364/400，奖励：-22.0，Epsilon：0.01\n", + "回合：365/400，奖励：-15.0，Epsilon：0.01\n", + "回合：366/400，奖励：-15.0，Epsilon：0.01\n", + "回合：367/400，奖励：-15.0，Epsilon：0.01\n", + "回合：368/400，奖励：-16.0，Epsilon：0.01\n", + "回合：369/400，奖励：-16.0，Epsilon：0.01\n", + "回合：370/400，奖励：-15.0，Epsilon：0.01\n", + "回合：371/400，奖励：-20.0，Epsilon：0.01\n", + "回合：372/400，奖励：-15.0，Epsilon：0.01\n", + "回合：373/400，奖励：-15.0，Epsilon：0.01\n", + "回合：374/400，奖励：-15.0，Epsilon：0.01\n", + "回合：375/400，奖励：-16.0，Epsilon：0.01\n", + "回合：376/400，奖励：-15.0，Epsilon：0.01\n", + "回合：377/400，奖励：-15.0，Epsilon：0.01\n", + "回合：378/400，奖励：-17.0，Epsilon：0.01\n", + "回合：379/400，奖励：-20.0，Epsilon：0.01\n", + "回合：380/400，奖励：-17.0，Epsilon：0.01\n", + "回合：381/400，奖励：-15.0，Epsilon：0.01\n", + "回合：382/400，奖励：-15.0，Epsilon：0.01\n", + "回合：383/400，奖励：-15.0，Epsilon：0.01\n", + "回合：384/400，奖励：-15.0，Epsilon：0.01\n", + "回合：385/400，奖励：-16.0，Epsilon：0.01\n", + "回合：386/400，奖励：-15.0，Epsilon：0.01\n", + "回合：387/400，奖励：-18.0，Epsilon：0.01\n", + "回合：388/400，奖励：-15.0，Epsilon：0.01\n", + "回合：389/400，奖励：-15.0，Epsilon：0.01\n", + "回合：390/400，奖励：-15.0，Epsilon：0.01\n", + "回合：391/400，奖励：-16.0，Epsilon：0.01\n", + "回合：392/400，奖励：-18.0，Epsilon：0.01\n", + "回合：393/400，奖励：-15.0，Epsilon：0.01\n", + "回合：394/400，奖励：-15.0，Epsilon：0.01\n", + "回合：395/400，奖励：-15.0，Epsilon：0.01\n", + "回合：396/400，奖励：-20.0，Epsilon：0.01\n", + "回合：397/400，奖励：-15.0，Epsilon：0.01\n", + "回合：398/400，奖励：-15.0，Epsilon：0.01\n", + "回合：399/400，奖励：-15.0，Epsilon：0.01\n", + "回合：400/400，奖励：-15.0，Epsilon：0.01\n", + "完成训练！\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "开始测试！\n", + "环境：CliffWalking-v0, 算法：Sarsa, 设备：cpu\n", + "回合数：1/20, 奖励：-15.0\n", + "回合数：2/20, 奖励：-15.0\n", + "回合数：3/20, 奖励：-15.0\n", + "回合数：4/20, 奖励：-15.0\n", + "回合数：5/20, 奖励：-15.0\n", + "回合数：6/20, 奖励：-15.0\n", + "回合数：7/20, 奖励：-15.0\n", + "回合数：8/20, 奖励：-15.0\n", + "回合数：9/20, 奖励：-15.0\n", + "回合数：10/20, 奖励：-15.0\n", + "回合数：11/20, 奖励：-15.0\n", + "回合数：12/20, 奖励：-15.0\n", + "回合数：13/20, 奖励：-15.0\n", + "回合数：14/20, 奖励：-15.0\n", + "回合数：15/20, 奖励：-15.0\n", + "回合数：16/20, 奖励：-15.0\n", + "回合数：17/20, 奖励：-15.0\n", + "回合数：18/20, 奖励：-15.0\n", + "回合数：19/20, 奖励：-15.0\n", + "回合数：20/20, 奖励：-15.0\n", + "完成测试！\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 获取参数\n", + "cfg = get_args() \n", + "# 训练\n", + "env, agent = env_agent_config(cfg)\n", + "res_dic = train(cfg, env, agent)\n", + " \n", + "plot_rewards(res_dic['rewards'], cfg, tag=\"train\") \n", + "# 测试\n", + "res_dic = test(cfg, env, agent)\n", + "plot_rewards(res_dic['rewards'], cfg, tag=\"test\") # 画出结果" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.7.12 ('rl_tutorials')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.12" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "4f613f1ab80ec98dc1b91d6e720de51301598a187317378e53e49b773c1123dd" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/projects/notebooks/3.DQN.ipynb b/projects/notebooks/3.DQN.ipynb new file mode 100644 index 0000000..9fed7b6 --- /dev/null +++ b/projects/notebooks/3.DQN.ipynb @@ -0,0 +1,277 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1、分析伪代码\n", + "\n", + "目前DQN算法基本遵循[Nature DQN](https://www.nature.com/articles/nature14236)的伪代码步骤，如下：\n", + "\n", + "
\n", + "\"\" \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1、定义算法\n", + "\n", + "教程中提到相比于Q learning，DQN本质上是为了适应更为复杂的环境，并且经过不断的改良迭代，到了Nature DQN（即Volodymyr Mnih发表的Nature论文）这里才算是基本完善。DQN主要改动的点有三个：\n", + "* 使用深度神经网络替代原来的Q表：这个很容易理解原因\n", + "* 使用了经验回放（Replay Buffer）：这个好处有很多，一个是使用一堆历史数据去训练，比之前用一次就扔掉好多了，大大提高样本效率，另外一个是面试常提到的，减少样本之间的相关性，原则上获取经验跟学习阶段是分开的，原来时序的训练数据有可能是不稳定的，打乱之后再学习有助于提高训练的稳定性，跟深度学习中划分训练测试集时打乱样本是一个道理。\n", + "* 使用了两个网络：即策略网络和目标网络，每隔若干步才把每步更新的策略网络参数复制给目标网络，这样做也是为了训练的稳定，避免Q值的估计发散。想象一下，如果当前有个transition（这个Q learning中提过的，一定要记住！！！）样本导致对Q值进行了较差的过估计，如果接下来从经验回放中提取到的样本正好连续几个都这样的，很有可能导致Q值的发散（它的青春小鸟一去不回来了）。再打个比方，我们玩RPG或者闯关类游戏，有些人为了破纪录经常Save和Load，只要我出了错，我不满意我就加载之前的存档，假设不允许加载呢，就像DQN算法一样训练过程中会退不了，这时候是不是搞两个档，一个档每帧都存一下，另外一个档打了不错的结果再存，也就是若干个间隔再存一下，到最后用间隔若干步数再存的档一般都比每帧都存的档好些呢。当然你也可以再搞更多个档，也就是DQN增加多个目标网络，但是对于DQN则没有多大必要，多几个网络效果不见得会好很多。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1、定义模型\n", + "\n", + "前面说了DQN的模型不再是Q表，而是一个深度神经网络，这里我只用了一个三层的全连接网络（FCN），这种网络也叫多层感知机（MLP），至于怎么用Torch写网络这里就不多说明了，以下仅供参考。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class MLP(nn.Module):\n", + " def __init__(self, n_states,n_actions,hidden_dim=128):\n", + " \"\"\" 初始化q网络，为全连接网络\n", + " n_states: 输入的特征数即环境的状态维度\n", + " n_actions: 输出的动作维度\n", + " \"\"\"\n", + " super(MLP, self).__init__()\n", + " self.fc1 = nn.Linear(n_states, hidden_dim) # 输入层\n", + " self.fc2 = nn.Linear(hidden_dim,hidden_dim) # 隐藏层\n", + " self.fc3 = nn.Linear(hidden_dim, n_actions) # 输出层\n", + " \n", + " def forward(self, x):\n", + " # 各层对应的激活函数\n", + " x = F.relu(self.fc1(x)) \n", + " x = F.relu(self.fc2(x))\n", + " return self.fc3(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2、定义经验回放\n", + "\n", + "经验回放首先是具有一定容量的，只有存储一定的transition网络才会更新，否则就退回到了之前的逐步更新了。另外写经验回放的时候一般需要包涵两个功能或方法，一个是push，即将一个transition样本按顺序放到经验回放中，如果满了就把最开始放进去的样本挤掉，因此如果大家学过数据结构的话推荐用队列来写，虽然这里不是。另外一个是sample，很简单就是随机采样出一个或者若干个（具体多少就是batch_size了）样本供DQN网络更新。功能讲清楚了，大家可以按照自己的想法用代码来实现，可以肯定地说，我这里不是最高效的，毕竟这还是青涩时期写出的代码。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class ReplayBuffer:\n", + " def __init__(self, capacity):\n", + " self.capacity = capacity # 经验回放的容量\n", + " self.buffer = [] # 缓冲区\n", + " self.position = 0 \n", + " \n", + " def push(self, state, action, reward, next_state, done):\n", + " ''' 缓冲区是一个队列，容量超出时去掉开始存入的转移(transition)\n", + " '''\n", + " if len(self.buffer) < self.capacity:\n", + " self.buffer.append(None)\n", + " self.buffer[self.position] = (state, action, reward, next_state, done)\n", + " self.position = (self.position + 1) % self.capacity \n", + " \n", + " def sample(self, batch_size):\n", + " batch = random.sample(self.buffer, batch_size) # 随机采出小批量转移\n", + " state, action, reward, next_state, done = zip(*batch) # 解压成状态，动作等\n", + " return state, action, reward, next_state, done\n", + " \n", + " def __len__(self):\n", + " ''' 返回当前存储的量\n", + " '''\n", + " return len(self.buffer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.3、真--定义算法\n", + "\n", + "到了高级一点的算法，定义算法就比较麻烦，要先定义一些子模块。可以看到，其实去掉子模块的话，DQN跟Q learning的算法结构没啥区别，当然因为神经网络一般需要Torch或者Tensorflow来写，因此推荐大家先去学一学这些工具，比如\"eat_pytorch_in_20_days\"。\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class DQN:\n", + " def __init__(self,n_actions,model,memory,cfg):\n", + "\n", + " self.n_actions = n_actions \n", + " self.device = torch.device(cfg.device) # cpu or cuda\n", + " self.gamma = cfg.gamma # 奖励的折扣因子\n", + " # e-greedy策略相关参数\n", + " self.sample_count = 0 # 用于epsilon的衰减计数\n", + " self.epsilon = lambda sample_count: cfg.epsilon_end + \\\n", + " (cfg.epsilon_start - cfg.epsilon_end) * \\\n", + " math.exp(-1. * sample_count / cfg.epsilon_decay)\n", + " self.batch_size = cfg.batch_size\n", + " self.policy_net = model.to(self.device)\n", + " self.target_net = model.to(self.device)\n", + " for target_param, param in zip(self.target_net.parameters(),self.policy_net.parameters()): # 复制参数到目标网路targe_net\n", + " target_param.data.copy_(param.data)\n", + " self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr) # 优化器\n", + " self.memory = memory # 经验回放\n", + "\n", + " def sample(self, state):\n", + " ''' 选择动作\n", + " '''\n", + " self.sample_count += 1\n", + " if random.random() > self.epsilon(self.sample_count):\n", + " with torch.no_grad():\n", + " state = torch.tensor(state, device=self.device, dtype=torch.float32).unsqueeze(dim=0)\n", + " q_values = self.policy_net(state)\n", + " action = q_values.max(1)[1].item() # 选择Q值最大的动作\n", + " else:\n", + " action = random.randrange(self.n_actions)\n", + " return action\n", + " def predict(self,state):\n", + " with torch.no_grad():\n", + " state = torch.tensor(state, device=self.device, dtype=torch.float32).unsqueeze(dim=0)\n", + " q_values = self.policy_net(state)\n", + " action = q_values.max(1)[1].item() # 选择Q值最大的动作\n", + " return action\n", + " def update(self):\n", + " if len(self.memory) < self.batch_size: # 当memory中不满足一个批量时，不更新策略\n", + " return\n", + " # 从经验回放中(replay memory)中随机采样一个批量的转移(transition)\n", + " \n", + " state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(\n", + " self.batch_size)\n", + " state_batch = torch.tensor(np.array(state_batch), device=self.device, dtype=torch.float)\n", + " action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(1) \n", + " reward_batch = torch.tensor(reward_batch, device=self.device, dtype=torch.float) \n", + " next_state_batch = torch.tensor(np.array(next_state_batch), device=self.device, dtype=torch.float)\n", + " done_batch = torch.tensor(np.float32(done_batch), device=self.device)\n", + " q_values = self.policy_net(state_batch).gather(dim=1, index=action_batch) # 计算当前状态(s_t,a)对应的Q(s_t, a)\n", + " next_q_values = self.target_net(next_state_batch).max(1)[0].detach() # 计算下一时刻的状态(s_t_,a)对应的Q值\n", + " # 计算期望的Q值，对于终止状态，此时done_batch[0]=1, 对应的expected_q_value等于reward\n", + " expected_q_values = reward_batch + self.gamma * next_q_values * (1-done_batch)\n", + " loss = nn.MSELoss()(q_values, expected_q_values.unsqueeze(1)) # 计算均方根损失\n", + " # 优化更新模型\n", + " self.optimizer.zero_grad() \n", + " loss.backward()\n", + " for param in self.policy_net.parameters(): # clip防止梯度爆炸\n", + " param.grad.data.clamp_(-1, 1)\n", + " self.optimizer.step() \n", + "\n", + " def save(self, path):\n", + " torch.save(self.target_net.state_dict(), path+'checkpoint.pth')\n", + "\n", + " def load(self, path):\n", + " self.target_net.load_state_dict(torch.load(path+'checkpoint.pth'))\n", + " for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):\n", + " param.data.copy_(target_param.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2、定义训练" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def train(cfg, env, agent):\n", + " ''' 训练\n", + " '''\n", + " print(\"开始训练！\")\n", + " print(f\"回合：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}\")\n", + " rewards = [] # 记录所有回合的奖励\n", + " steps = []\n", + " for i_ep in range(cfg.train_eps):\n", + " ep_reward = 0 # 记录一回合内的奖励\n", + " ep_step = 0\n", + " state = env.reset() # 重置环境，返回初始状态\n", + " while True:\n", + " ep_step += 1\n", + " action = agent.sample(state) # 选择动作\n", + " next_state, reward, done, _ = env.step(action) # 更新环境，返回transition\n", + " agent.memory.push(state, action, reward,\n", + " next_state, done) # 保存transition\n", + " state = next_state # 更新下一个状态\n", + " agent.update() # 更新智能体\n", + " ep_reward += reward # 累加奖励\n", + " if done:\n", + " break\n", + " if (i_ep + 1) % cfg.target_update == 0: # 智能体目标网络更新\n", + " agent.target_net.load_state_dict(agent.policy_net.state_dict())\n", + " steps.append(ep_step)\n", + " rewards.append(ep_reward)\n", + " if (i_ep + 1) % 10 == 0:\n", + " print(f'回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward:.2f}，Epislon：{agent.epsilon(agent.frame_idx):.3f}')\n", + " print(\"完成训练！\")\n", + " env.close()\n", + " res_dic = {'rewards':rewards}\n", + " return res_dic\n", + "\n", + "def test(cfg, env, agent):\n", + " print(\"开始测试！\")\n", + " print(f\"回合：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}\")\n", + " rewards = [] # 记录所有回合的奖励\n", + " steps = []\n", + " for i_ep in range(cfg.test_eps):\n", + " ep_reward = 0 # 记录一回合内的奖励\n", + " ep_step = 0\n", + " state = env.reset() # 重置环境，返回初始状态\n", + " while True:\n", + " ep_step+=1\n", + " action = agent.predict(state) # 选择动作\n", + " next_state, reward, done, _ = env.step(action) # 更新环境，返回transition\n", + " state = next_state # 更新下一个状态\n", + " ep_reward += reward # 累加奖励\n", + " if done:\n", + " break\n", + " steps.append(ep_step)\n", + " rewards.append(ep_reward)\n", + " print(f'回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward:.2f}')\n", + " print(\"完成测试\")\n", + " env.close()\n", + " return {'rewards':rewards}" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.7.13 ('easyrl')", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.7.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "8994a120d39b6e6a2ecc94b4007f5314b68aa69fc88a7f00edf21be39b41f49c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/projects/notebooks/DQN.ipynb b/projects/notebooks/DQN.ipynb deleted file mode 100644 index fba9f1f..0000000 --- a/projects/notebooks/DQN.ipynb +++ /dev/null @@ -1,36 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1、分析伪代码\n", - "\n", - "目前DQN算法基本遵循[Nature DQN](https://www.nature.com/articles/nature14236)的伪代码步骤，如下：\n", - "\n", - "
\n", - "\"\" \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "language_info": { - "name": "python" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/projects/notebooks/QLearning.ipynb b/projects/notebooks/QLearning.ipynb deleted file mode 100644 index a610084..0000000 --- a/projects/notebooks/QLearning.ipynb +++ /dev/null @@ -1,19 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "language_info": { - "name": "python" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/projects/requirements.txt b/projects/requirements.txt new file mode 100644 index 0000000..42e65c6 --- /dev/null +++ b/projects/requirements.txt @@ -0,0 +1,10 @@ +gym==0.21.0 +torch==1.9.0 +torchvision==0.10.0 +torchaudio==0.9.0 +ipykernel==6.15.1 +jupyter==1.0.0 +matplotlib==3.5.2 +seaborn==0.11.2 +dill==0.3.5.1 +argparse==1.4.0 \ No newline at end of file

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