116 lines
4.6 KiB
Python
116 lines
4.6 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: 2021-03-11 19:22:50
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Discription:
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Environment:
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'''
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import sys,os
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sys.path.append(os.getcwd()) # 添加当前终端路径
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import argparse
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import gym
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import datetime
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from QLearning.plot import plot
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from QLearning.utils import save_results
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from envs.gridworld_env import CliffWalkingWapper, FrozenLakeWapper
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from QLearning.agent import QLearning
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SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
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SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/'
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RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/result/"+SEQUENCE+'/'
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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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'''训练相关参数'''
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parser.add_argument("--n_episodes", default=500,
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type=int, help="训练的最大episode数目")
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'''算法相关参数'''
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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=0.99,
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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=0.01,
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type=float, help="e-greedy策略中的结束epsilon")
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parser.add_argument("--epsilon_decay", default=200,
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type=float, help="e-greedy策略中epsilon的衰减率")
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parser.add_argument("--lr", default=0.1, type=float, help="学习率")
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config = parser.parse_args()
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return config
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def train(cfg,env,agent):
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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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rewards = [] # 记录所有episode的reward,
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steps = [] # 记录所有episode的steps
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for i_episode in range(cfg.n_episodes):
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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.choose_action(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.update(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 done:
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break
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steps.append(ep_steps)
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# 计算滑动平均的reward
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if rewards:
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rewards.append(rewards[-1]*0.9+ep_reward*0.1)
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else:
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rewards.append(ep_reward)
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print("Episode:{}/{}: reward:{:.1f}".format(i_episode+1, cfg.n_episodes,ep_reward))
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plot(rewards)
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if not os.path.exists(SAVED_MODEL_PATH):
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os.mkdir(SAVED_MODEL_PATH)
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agent.save(SAVED_MODEL_PATH+'Q_table.pkl') # 训练结束，保存模型
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'''存储reward等相关结果'''
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save_results(rewards,tag='train',result_path=RESULT_PATH)
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def eval(cfg,env,agent):
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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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rewards = [] # 记录所有episode的reward,
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steps = [] # 记录所有episode的steps
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for i_episode in range(20):
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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.choose_action(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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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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# 计算滑动平均的reward
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if rewards:
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rewards.append(rewards[-1]*0.9+ep_reward*0.1)
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else:
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rewards.append(ep_reward)
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print("Episode:{}/{}: reward:{:.1f}".format(i_episode+1, cfg.n_episodes,ep_reward))
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plot(rewards)
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'''存储reward等相关结果'''
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save_results(rewards,tag='eval',result_path=RESULT_PATH)
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if __name__ == "__main__":
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cfg = get_args()
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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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n_actions = env.action_space.n
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agent = QLearning(n_actions,cfg)
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train(cfg,env,agent)
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eval(cfg,env,agent)
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