114 lines
4.4 KiB
Python
114 lines
4.4 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: 2021-03-31 18:14:59
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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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curr_path = os.path.dirname(__file__)
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parent_path=os.path.dirname(curr_path)
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sys.path.append(parent_path) # add current terminal path to sys.path
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import gym
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import datetime
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from envs.gridworld_env import CliffWalkingWapper, FrozenLakeWapper
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from QLearning.agent import QLearning
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from common.plot import plot_rewards
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from common.utils import save_results
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SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
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SAVED_MODEL_PATH = curr_path+"/saved_model/"+SEQUENCE+'/' # path to save model
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if not os.path.exists(curr_path+"/saved_model/"):
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os.mkdir(curr_path+"/saved_model/")
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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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RESULT_PATH = curr_path+"/results/"+SEQUENCE+'/' # path to save rewards
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if not os.path.exists(curr_path+"/results/"):
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os.mkdir(curr_path+"/results/")
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if not os.path.exists(RESULT_PATH):
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os.mkdir(RESULT_PATH)
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class QlearningConfig:
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'''训练相关参数'''
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def __init__(self):
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self.train_eps = 200 # 训练的episode数目
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self.gamma = 0.9 # reward的衰减率
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self.epsilon_start = 0.99 # e-greedy策略中初始epsilon
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self.epsilon_end = 0.01 # e-greedy策略中的终止epsilon
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self.epsilon_decay = 200 # e-greedy策略中epsilon的衰减率
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self.lr = 0.1 # learning rate
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def train(cfg,env,agent):
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rewards = []
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ma_rewards = [] # moving average reward
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steps = [] # 记录所有episode的steps
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for i_episode in range(cfg.train_eps):
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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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state = 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(state) # 根据算法选择一个动作
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next_state, reward, done, _ = env.step(action) # 与环境进行一次动作交互
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agent.update(state, action, reward, next_state, done) # Q-learning算法更新
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state = next_state # 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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rewards.append(ep_reward)
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if ma_rewards:
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ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
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else:
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ma_rewards.append(ep_reward)
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print("Episode:{}/{}: reward:{:.1f}".format(i_episode+1, cfg.train_eps,ep_reward))
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return rewards,ma_rewards
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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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ma_rewards = [] # 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(cfg.train_eps):
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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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state = 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(state) # 根据算法选择一个动作
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next_state, reward, done, _ = env.step(action) # 与环境进行一个交互
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state = next_state # 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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rewards.append(ep_reward)
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# 计算滑动平均的reward
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if ma_rewards:
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ma_rewards.append(rewards[-1]*0.9+ep_reward*0.1)
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else:
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ma_rewards.append(ep_reward)
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print("Episode:{}/{}: reward:{:.1f}".format(i_episode+1, cfg.train_eps,ep_reward))
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return rewards,ma_rewards
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if __name__ == "__main__":
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cfg = QlearningConfig()
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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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action_dim = env.action_space.n
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agent = QLearning(action_dim,cfg)
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rewards,ma_rewards = train(cfg,env,agent)
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agent.save(path=SAVED_MODEL_PATH)
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save_results(rewards,ma_rewards,tag='train',path=RESULT_PATH)
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plot_rewards(rewards,ma_rewards,tag="train",algo = "On-Policy First-Visit MC Control",path=RESULT_PATH)
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