90 lines
3.4 KiB
Python
90 lines
3.4 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: 2020-11-22 23:21:53
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LastEditor: John
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LastEditTime: 2021-03-13 11:50:32
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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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from itertools import count
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import datetime
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import gym
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from PolicyGradient.agent import PolicyGradient
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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") # 获取当前时间
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SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/' # 生成保存的模型路径
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if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"): # 检测是否存在文件夹
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os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/")
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if not os.path.exists(SAVED_MODEL_PATH): # 检测是否存在文件夹
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os.mkdir(SAVED_MODEL_PATH)
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RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/results/"+SEQUENCE+'/' # 存储reward的路径
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if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/results/"): # 检测是否存在文件夹
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os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/results/")
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if not os.path.exists(RESULT_PATH): # 检测是否存在文件夹
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os.mkdir(RESULT_PATH)
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class PGConfig:
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def __init__(self):
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self.train_eps = 300 # 训练的episode数目
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self.batch_size = 8
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self.lr = 0.01 # 学习率
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self.gamma = 0.99
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self.hidden_dim = 36 # 隐藏层维度
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def train(cfg,env,agent):
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'''下面带pool都是存放的transition序列用于gradient'''
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state_pool = [] # 存放每batch_size个episode的state序列
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action_pool = []
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reward_pool = []
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''' 存储每个episode的reward用于绘图'''
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rewards = []
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ma_rewards = []
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for i_episode in range(cfg.train_eps):
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state = env.reset()
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ep_reward = 0
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for _ in count():
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action = agent.choose_action(state) # 根据当前环境state选择action
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next_state, reward, done, _ = env.step(action)
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ep_reward += reward
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if done:
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reward = 0
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state_pool.append(state)
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action_pool.append(float(action))
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reward_pool.append(reward)
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state = next_state
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if done:
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print('Episode:', i_episode, ' Reward:', ep_reward)
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break
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if i_episode > 0 and i_episode % cfg.batch_size == 0:
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agent.update(reward_pool,state_pool,action_pool)
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state_pool = [] # 每个episode的state
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action_pool = []
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reward_pool = []
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rewards.append(ep_reward)
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if ma_rewards:
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ma_rewards.append(
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0.9*ma_rewards[-1]+0.1*ep_reward)
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else:
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ma_rewards.append(ep_reward)
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print('complete training！')
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return rewards, ma_rewards
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if __name__ == "__main__":
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cfg = PGConfig()
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env = gym.make('CartPole-v0') # 可google为什么unwrapped gym，此处一般不需要
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env.seed(1) # 设置env随机种子
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n_states = env.observation_space.shape[0]
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n_actions = env.action_space.n
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agent = PolicyGradient(n_states,cfg)
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rewards, ma_rewards = train(cfg,env,agent)
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agent.save_model(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 = "Policy Gradient",path=RESULT_PATH)
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