153 lines
5.5 KiB
Python
153 lines
5.5 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: 2022-02-10 06:13:21
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Discription:
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Environment:
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'''
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import sys
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import os
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curr_path = os.path.dirname(os.path.abspath(__file__)) # 当前文件所在绝对路径
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parent_path = os.path.dirname(curr_path) # 父路径
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sys.path.append(parent_path) # 添加路径到系统路径
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import gym
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import torch
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import datetime
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from itertools import count
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from pg import PolicyGradient
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from common.utils import save_results, make_dir
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from common.utils import plot_rewards
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curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
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class Config:
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'''超参数
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'''
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def __init__(self):
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################################## 环境超参数 ###################################
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self.algo_name = "PolicyGradient" # 算法名称
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self.env_name = 'CartPole-v0' # 环境名称
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self.device = torch.device(
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"cuda" if torch.cuda.is_available() else "cpu") # 检测GPUgjgjlkhfsf风刀霜的撒发十
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self.seed = 10 # 随机种子，置0则不设置随机种子
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self.train_eps = 300 # 训练的回合数
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self.test_eps = 30 # 测试的回合数
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################################################################################
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################################## 算法超参数 ###################################
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self.batch_size = 8 # mini-batch SGD中的批量大小
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self.lr = 0.01 # 学习率
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self.gamma = 0.99 # 强化学习中的折扣因子
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self.hidden_dim = 36 # 网络隐藏层
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################################################################################
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################################# 保存结果相关参数 ################################
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self.result_path = curr_path + "/outputs/" + self.env_name + \
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'/' + curr_time + '/results/' # 保存结果的路径
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self.model_path = curr_path + "/outputs/" + self.env_name + \
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'/' + curr_time + '/models/' # 保存模型的路径
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self.save = True # 是否保存图片
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################################################################################
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def env_agent_config(cfg,seed=1):
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env = gym.make(cfg.env_name)
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env.seed(seed)
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n_states = env.observation_space.shape[0]
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agent = PolicyGradient(n_states,cfg)
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return env,agent
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def train(cfg,env,agent):
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print('开始训练!')
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print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')
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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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rewards = []
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ma_rewards = []
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for i_ep 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('回合：{}/{}, 奖励：{}'.format(i_ep + 1, cfg.train_eps, ep_reward))
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break
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if i_ep > 0 and i_ep % 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('完成训练！')
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env.close()
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return rewards, ma_rewards
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def test(cfg,env,agent):
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print('开始测试!')
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print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')
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rewards = []
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ma_rewards = []
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for i_ep in range(cfg.test_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 = next_state
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if done:
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print('回合：{}/{}, 奖励：{}'.format(i_ep + 1, cfg.train_eps, ep_reward))
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break
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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('完成测试！')
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env.close()
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return rewards, ma_rewards
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if __name__ == "__main__":
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cfg = Config()
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# 训练
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env, agent = env_agent_config(cfg)
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rewards, ma_rewards = train(cfg, env, agent)
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make_dir(cfg.result_path, cfg.model_path) # 创建保存结果和模型路径的文件夹
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agent.save(path=cfg.model_path) # 保存模型
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save_results(rewards, ma_rewards, tag='train',
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path=cfg.result_path) # 保存结果
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plot_rewards(rewards, ma_rewards, cfg, tag="train") # 画出结果
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# 测试
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env, agent = env_agent_config(cfg)
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agent.load(path=cfg.model_path) # 导入模型
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rewards, ma_rewards = test(cfg, env, agent)
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save_results(rewards, ma_rewards, tag='test',
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path=cfg.result_path) # 保存结果
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plot_rewards(rewards, ma_rewards, cfg, tag="test") # 画出结果
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