131 lines
5.3 KiB
Python
131 lines
5.3 KiB
Python
import sys,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 numpy as np
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import datetime
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from common.utils import plot_rewards
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from common.utils import save_results,make_dir
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from ppo2 import PPO
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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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def __init__(self) -> None:
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################################## 环境超参数 ###################################
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self.algo_name = "PPO" # 算法名称
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self.env_name = 'CartPole-v0' # 环境名称
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self.continuous = False # 环境是否为连续动作
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测GPU
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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 = 200 # 训练的回合数
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self.test_eps = 20 # 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 = 5 # mini-batch SGD中的批量大小
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self.gamma = 0.95 # 强化学习中的折扣因子
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self.n_epochs = 4
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self.actor_lr = 0.0003 # actor的学习率
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self.critic_lr = 0.0003 # critic的学习率
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self.gae_lambda = 0.95
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self.policy_clip = 0.2
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self.hidden_dim = 256
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self.update_fre = 20 # 策略更新频率
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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):
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''' 创建环境和智能体
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'''
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env = gym.make(cfg.env_name) # 创建环境
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n_states = env.observation_space.shape[0] # 状态维度
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if cfg.continuous:
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n_actions = env.action_space.shape[0] # 动作维度
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else:
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n_actions = env.action_space.n # 动作维度
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agent = PPO(n_states, n_actions, cfg) # 创建智能体
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if cfg.seed !=0: # 设置随机种子
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torch.manual_seed(cfg.seed)
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env.seed(cfg.seed)
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np.random.seed(cfg.seed)
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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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rewards = [] # 记录所有回合的奖励
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ma_rewards = [] # 记录所有回合的滑动平均奖励
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steps = 0
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for i_ep in range(cfg.train_eps):
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state = env.reset()
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done = False
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ep_reward = 0
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while not done:
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action, prob, val = agent.choose_action(state)
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state_, reward, done, _ = env.step(action)
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steps += 1
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ep_reward += reward
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agent.memory.push(state, action, prob, val, reward, done)
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if steps % cfg.update_fre == 0:
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agent.update()
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state = state_
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rewards.append(ep_reward)
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if ma_rewards:
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ma_rewards.append(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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if (i_ep+1)%10 == 0:
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print(f"回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward:.2f}")
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print('完成训练！')
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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 = [] # 记录所有回合的奖励
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ma_rewards = [] # 记录所有回合的滑动平均奖励
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for i_ep in range(cfg.test_eps):
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state = env.reset()
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done = False
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ep_reward = 0
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while not done:
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action, prob, val = agent.choose_action(state)
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state_, reward, done, _ = env.step(action)
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ep_reward += reward
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state = state_
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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('回合：{}/{}, 奖励：{}'.format(i_ep+1, cfg.test_eps, ep_reward))
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print('完成训练！')
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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)
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save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
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plot_rewards(rewards, ma_rewards, cfg, tag="train")
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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)
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rewards,ma_rewards = test(cfg,env,agent)
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save_results(rewards,ma_rewards,tag='test',path=cfg.result_path)
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plot_rewards(rewards,ma_rewards,cfg,tag="test") |