146 lines
5.6 KiB
Python
146 lines
5.6 KiB
Python
#!/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: 2021-01-05 09:41:34
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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 env 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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from env import env_init_1
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from params import get_args
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from params import SEQUENCE, SAVED_MODEL_PATH, RESULT_PATH
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from utils import save_results,save_model
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from plot import plot
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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 = env_init_1()
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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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print('Complete training！')
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save_model(agent,model_path=SAVED_MODEL_PATH)
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'''存储reward等相关结果'''
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save_results(rewards,MA_rewards,tag='train',result_path=RESULT_PATH)
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plot(rewards)
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plot(MA_rewards,ylabel='moving_average_rewards_train')
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def eval(cfg, saved_model_path = SAVED_MODEL_PATH):
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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_model(saved_model_path+'checkpoint.npy') # 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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print('Complete training！')
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save_model(agent,model_path=SAVED_MODEL_PATH)
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'''存储reward等相关结果'''
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save_results(rewards,MA_rewards,tag='train',result_path=RESULT_PATH)
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plot(rewards)
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plot(MA_rewards,ylabel='moving_average_rewards_train')
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if __name__ == "__main__":
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cfg = get_args()
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if cfg.train:
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train(cfg)
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eval(cfg)
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else:
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model_path = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"
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eval(cfg,saved_model_path=model_path) |