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JohnJim0816
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codes/Q-learning/main.py
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codes/Q-learning/main.py
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#!/usr/bin/env python
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# coding=utf-8
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'''
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Author: John
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Email: johnjim0816@gmail.com
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Date: 2020-09-11 23:03:00
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LastEditor: John
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LastEditTime: 2020-10-07 21:05:33
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Discription:
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Environment:
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'''
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# -*- coding: utf-8 -*-
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import gym
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from gridworld import CliffWalkingWapper, FrozenLakeWapper
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from agent import QLearning
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import os
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import numpy as np
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import argparse
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import time
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import matplotlib.pyplot as plt
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def get_args():
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'''训练的模型参数
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'''
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parser = argparse.ArgumentParser()
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parser.add_argument("--gamma", default=0.9,
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type=float, help="reward 的衰减率")
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parser.add_argument("--epsilon_start", default=0.9,
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type=float,help="e-greedy策略中初始epsilon")
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parser.add_argument("--epsilon_end", default=0.1, type=float,help="e-greedy策略中的结束epsilon")
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parser.add_argument("--epsilon_decay", default=200, type=float,help="e-greedy策略中epsilon的衰减率")
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parser.add_argument("--policy_lr", default=0.1, type=float,help="学习率")
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parser.add_argument("--max_episodes", default=500, type=int,help="训练的最大episode数目")
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config = parser.parse_args()
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return config
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def train(cfg):
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# env = gym.make("FrozenLake-v0", is_slippery=False) # 0 left, 1 down, 2 right, 3 up
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# env = FrozenLakeWapper(env)
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env = gym.make("CliffWalking-v0") # 0 up, 1 right, 2 down, 3 left
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env = CliffWalkingWapper(env)
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agent = QLearning(
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obs_dim=env.observation_space.n,
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action_dim=env.action_space.n,
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learning_rate=cfg.policy_lr,
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gamma=cfg.gamma,
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epsilon_start=cfg.epsilon_start,epsilon_end=cfg.epsilon_end,epsilon_decay=cfg.epsilon_decay)
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render = False # 是否打开GUI画面
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rewards = [] # 记录所有episode的reward
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MA_rewards = [] # 记录滑动平均的reward
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steps = []# 记录所有episode的steps
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for i_episode in range(1,cfg.max_episodes+1):
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ep_reward = 0 # 记录每个episode的reward
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ep_steps = 0 # 记录每个episode走了多少step
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obs = env.reset() # 重置环境, 重新开一局（即开始新的一个episode）
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while True:
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action = agent.sample(obs) # 根据算法选择一个动作
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next_obs, reward, done, _ = env.step(action) # 与环境进行一个交互
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# 训练 Q-learning算法
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agent.learn(obs, action, reward, next_obs, done) # 不需要下一步的action
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obs = next_obs # 存储上一个观察值
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ep_reward += reward
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ep_steps += 1 # 计算step数
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if render:
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env.render() #渲染新的一帧图形
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if done:
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break
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steps.append(ep_steps)
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rewards.append(ep_reward)
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# 计算滑动平均的reward
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if i_episode == 1:
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MA_rewards.append(ep_reward)
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else:
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MA_rewards.append(
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0.9*MA_rewards[-1]+0.1*ep_reward)
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print('Episode %s: steps = %s , reward = %.1f, explore = %.2f' % (i_episode, ep_steps,
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ep_reward,agent.epsilon))
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# 每隔20个episode渲染一下看看效果
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if i_episode % 20 == 0:
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render = True
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else:
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render = False
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agent.save() # 训练结束，保存模型
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output_path = os.path.dirname(__file__)+"/result/"
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# 检测是否存在文件夹
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if not os.path.exists(output_path):
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os.mkdir(output_path)
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np.save(output_path+"rewards_train.npy", rewards)
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np.save(output_path+"MA_rewards_train.npy", MA_rewards)
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np.save(output_path+"steps_train.npy", steps)
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def test(cfg):
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env = gym.make("CliffWalking-v0") # 0 up, 1 right, 2 down, 3 left
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env = CliffWalkingWapper(env)
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agent = QLearning(
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obs_dim=env.observation_space.n,
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action_dim=env.action_space.n,
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learning_rate=cfg.policy_lr,
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gamma=cfg.gamma,
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epsilon_start=cfg.epsilon_start,epsilon_end=cfg.epsilon_end,epsilon_decay=cfg.epsilon_decay)
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agent.load() # 导入保存的模型
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rewards = [] # 记录所有episode的reward
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MA_rewards = [] # 记录滑动平均的reward
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steps = []# 记录所有episode的steps
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for i_episode in range(1,10+1):
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ep_reward = 0 # 记录每个episode的reward
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ep_steps = 0 # 记录每个episode走了多少step
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obs = env.reset() # 重置环境, 重新开一局（即开始新的一个episode）
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while True:
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action = agent.predict(obs) # 根据算法选择一个动作
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next_obs, reward, done, _ = env.step(action) # 与环境进行一个交互
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obs = next_obs # 存储上一个观察值
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time.sleep(0.5)
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env.render()
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ep_reward += reward
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ep_steps += 1 # 计算step数
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if done:
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break
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steps.append(ep_steps)
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rewards.append(ep_reward)
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# 计算滑动平均的reward
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if i_episode == 1:
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MA_rewards.append(ep_reward)
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else:
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MA_rewards.append(
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0.9*MA_rewards[-1]+0.1*ep_reward)
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print('Episode %s: steps = %s , reward = %.1f' % (i_episode, ep_steps, ep_reward))
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plt.plot(MA_rewards)
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plt.show()
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def main():
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cfg = get_args()
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# train(cfg)
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test(cfg)
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if __name__ == "__main__":
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main()
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