{ "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.11" }, "orig_nbformat": 2, "kernelspec": { "name": "python3", "display_name": "Python 3.7.11 64-bit ('py37': conda)" }, "interpreter": { "hash": "fbea1422c2cf61ed9c0cfc03f38f71cc9083cc288606edc4170b5309b352ce27" } }, "nbformat": 4, "nbformat_minor": 2, "cells": [ { "cell_type": "code", "execution_count": 1, "source": [ "import sys\n", "from pathlib import Path\n", "curr_path = str(Path().absolute())\n", "parent_path = str(Path().absolute().parent)\n", "sys.path.append(parent_path) # add current terminal path to sys.path\n", "\n", "import gym\n", "import datetime\n", "\n", "from envs.gridworld_env import CliffWalkingWapper\n", "from QLearning.agent import QLearning\n", "from common.plot import plot_rewards\n", "from common.utils import save_results,make_dir\n", "curr_time = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\") # obtain current time" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 2, "source": [ "class QlearningConfig:\n", " '''训练相关参数'''\n", " def __init__(self):\n", " self.algo = 'Qlearning'\n", " self.env = 'CliffWalking-v0' # 0 up, 1 right, 2 down, 3 left\n", " self.result_path = curr_path+\"/outputs/\" +self.env+'/'+curr_time+'/results/' # path to save results\n", " self.model_path = curr_path+\"/outputs/\" +self.env+'/'+curr_time+'/models/' # path to save models\n", " self.train_eps = 200 # 训练的episode数目\n", " self.eval_eps = 30\n", " self.gamma = 0.9 # reward的衰减率\n", " self.epsilon_start = 0.95 # e-greedy策略中初始epsilon\n", " self.epsilon_end = 0.01 # e-greedy策略中的终止epsilon\n", " self.epsilon_decay = 200 # e-greedy策略中epsilon的衰减率\n", " self.lr = 0.1 # learning rate" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 3, "source": [ "def env_agent_config(cfg,seed=1):\n", " env = gym.make(cfg.env) \n", " env = CliffWalkingWapper(env)\n", " env.seed(seed)\n", " state_dim = env.observation_space.n\n", " action_dim = env.action_space.n\n", " agent = QLearning(state_dim,action_dim,cfg)\n", " return env,agent" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 4, "source": [ "def train(cfg,env,agent):\n", " rewards = [] \n", " ma_rewards = [] # moving average reward\n", " for i_ep in range(cfg.train_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episode）\n", " while True:\n", " action = agent.choose_action(state) # 根据算法选择一个动作\n", " next_state, reward, done, _ = env.step(action) # 与环境进行一次动作交互\n", " agent.update(state, action, reward, next_state, done) # Q-learning算法更新\n", " state = next_state # 存储上一个观察值\n", " ep_reward += reward\n", " if done:\n", " break\n", " rewards.append(ep_reward)\n", " if ma_rewards:\n", " ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)\n", " else:\n", " ma_rewards.append(ep_reward)\n", " if (i_ep+1)%10==0:\n", " print(\"Episode:{}/{}: reward:{:.1f}\".format(i_ep+1, cfg.train_eps,ep_reward))\n", " return rewards,ma_rewards" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 5, "source": [ "def eval(cfg,env,agent):\n", " # env = gym.make(\"FrozenLake-v0\", is_slippery=False) # 0 left, 1 down, 2 right, 3 up\n", " # env = FrozenLakeWapper(env)\n", " rewards = [] # 记录所有episode的reward\n", " ma_rewards = [] # 滑动平均的reward\n", " for i_ep in range(cfg.eval_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episode）\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 # 存储上一个观察值\n", " ep_reward += reward\n", " if done:\n", " break\n", " rewards.append(ep_reward)\n", " if ma_rewards:\n", " ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)\n", " else:\n", " ma_rewards.append(ep_reward)\n", " if (i_ep+1)%10==0:\n", " print(f\"Episode:{i_ep+1}/{cfg.eval_eps}, reward:{ep_reward:.1f}\")\n", " return rewards,ma_rewards" ], "outputs": [], "metadata": {} }, { "cell_type": "code", "execution_count": 6, "source": [ "cfg = QlearningConfig()\n", "env,agent = env_agent_config(cfg,seed=1)\n", "rewards,ma_rewards = train(cfg,env,agent)\n", "make_dir(cfg.result_path,cfg.model_path)\n", "agent.save(path=cfg.model_path)\n", "save_results(rewards,ma_rewards,tag='train',path=cfg.result_path)\n", "plot_rewards(rewards,ma_rewards,tag=\"train\",env=cfg.env,algo = cfg.algo,path=cfg.result_path)\n", "\n", "env,agent = env_agent_config(cfg,seed=10)\n", "agent.load(path=cfg.model_path)\n", "rewards,ma_rewards = eval(cfg,env,agent)\n", "save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)\n", "plot_rewards(rewards,ma_rewards,tag=\"eval\",env=cfg.env,algo = cfg.algo,path=cfg.result_path)" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Episode:10/200: reward:-287.0\n", "Episode:20/200: reward:-142.0\n", "Episode:30/200: reward:-67.0\n", "Episode:40/200: reward:-61.0\n", "Episode:50/200: reward:-74.0\n", "Episode:60/200: reward:-41.0\n", "Episode:70/200: reward:-55.0\n", "Episode:80/200: reward:-66.0\n", "Episode:90/200: reward:-31.0\n", "Episode:100/200: reward:-31.0\n", "Episode:110/200: reward:-58.0\n", "Episode:120/200: reward:-25.0\n", "Episode:130/200: reward:-18.0\n", "Episode:140/200: reward:-27.0\n", "Episode:150/200: reward:-28.0\n", "Episode:160/200: reward:-25.0\n", "Episode:170/200: reward:-35.0\n", "Episode:180/200: reward:-13.0\n", "Episode:190/200: reward:-22.0\n", "Episode:200/200: reward:-26.0\n", "保存模型成功！\n", "结果保存完毕！\n" ] }, { "output_type": "display_data", "data": { "image/png": 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", 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