update projects

codes/PPO/task0.py

@@ -1,131 +0,0 @@
-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 gym
-import torch
-import numpy as np
-import datetime
-from common.utils import plot_rewards
-from common.utils import save_results,make_dir
-from ppo2 import PPO
-
-curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # 获取当前时间
-
-class Config:
-    def __init__(self) -> None:
-        ################################## 环境超参数 ###################################
-        self.algo_name = "PPO"  # 算法名称
-        self.env_name = 'CartPole-v0' # 环境名称
-        self.continuous = False # 环境是否为连续动作
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  # 检测GPU
-        self.seed = 10 # 随机种子，置0则不设置随机种子
-        self.train_eps = 200 # 训练的回合数
-        self.test_eps = 20 # 测试的回合数
-        ################################################################################
-        
-        ################################## 算法超参数 ####################################
-        self.batch_size = 5  # mini-batch SGD中的批量大小
-        self.gamma = 0.95  # 强化学习中的折扣因子
-        self.n_epochs = 4
-        self.actor_lr = 0.0003 # actor的学习率
-        self.critic_lr = 0.0003 # critic的学习率
-        self.gae_lambda = 0.95
-        self.policy_clip = 0.2
-        self.hidden_dim = 256
-        self.update_fre = 20 # 策略更新频率
-        ################################################################################
-        
-        ################################# 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 env_agent_config(cfg):
-    ''' 创建环境和智能体
-    '''
-    env = gym.make(cfg.env_name)  # 创建环境
-    n_states = env.observation_space.shape[0]  # 状态维度
-    if cfg.continuous:
-        n_actions = env.action_space.shape[0] # 动作维度
-    else:
-        n_actions = env.action_space.n  # 动作维度
-    agent = PPO(n_states, n_actions, cfg)  # 创建智能体
-    if cfg.seed !=0: # 设置随机种子
-        torch.manual_seed(cfg.seed)
-        env.seed(cfg.seed)
-        np.random.seed(cfg.seed)
-    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 = [] # 记录所有回合的奖励
-    ma_rewards = []  # 记录所有回合的滑动平均奖励
-    steps = 0
-    for i_ep in range(cfg.train_eps):
-        state = env.reset()
-        done = False
-        ep_reward = 0
-        while not done:
-            action, prob, val = agent.choose_action(state)
-            state_, reward, done, _ = env.step(action)
-            steps += 1
-            ep_reward += reward
-            agent.memory.push(state, action, prob, val, reward, done)
-            if steps % cfg.update_fre == 0:
-                agent.update()
-            state = state_
-        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)
-        if (i_ep+1)%10 == 0: 
-            print(f"回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward:.2f}")
-    print('完成训练！')
-    return rewards,ma_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 = []  # 记录所有回合的滑动平均奖励
-    for i_ep in range(cfg.test_eps):
-        state = env.reset()
-        done = False
-        ep_reward = 0
-        while not done:
-            action, prob, val = agent.choose_action(state)
-            state_, reward, done, _ = env.step(action)
-            ep_reward += reward
-            state = state_
-        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('回合：{}/{}, 奖励：{}'.format(i_ep+1, cfg.test_eps, ep_reward))
-    print('完成训练！')
-    return rewards,ma_rewards
-
-if __name__ == "__main__":
-    cfg  = Config()
-    # 训练
-    env,agent = env_agent_config(cfg)
-    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)
-    save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
-    plot_rewards(rewards, ma_rewards, cfg, tag="train")
-    # 测试
-    env,agent = env_agent_config(cfg)
-    agent.load(path=cfg.model_path)
-    rewards,ma_rewards = test(cfg,env,agent)
-    save_results(rewards,ma_rewards,tag='test',path=cfg.result_path)
-    plot_rewards(rewards,ma_rewards,cfg,tag="test")

projects/.gitignore

@@ -0,0 +1,5 @@
+.DS_Store
+.ipynb_checkpoints
+__pycache__
+.vscode
+test.py

projects/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020 John Jim
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

projects/README.md

@@ -0,0 +1,49 @@
+## 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**。
+
+代码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
+
+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：
+```bash
+conda env create -f environment.yaml
+```
+## 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)