121 lines
4.7 KiB
Python
121 lines
4.7 KiB
Python
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}, 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 eval(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}, 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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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 datetime
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from common.plot import plot_rewards
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from common.utils import save_results,make_dir
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from PPO.agent import PPO
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from PPO.train import train
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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 PPOConfig:
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def __init__(self) -> None:
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self.algo = "DQN" # 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.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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self.batch_size = 5
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self.gamma=0.99
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self.n_epochs = 4
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self.actor_lr = 0.0003
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self.critic_lr = 0.0003
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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 # frequency of agent update
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class PlotConfig:
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def __init__(self) -> None:
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self.algo = "DQN" # 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("cuda" if torch.cuda.is_available() else "cpu") # 检测GPU
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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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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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n_actions = env.action_space.n
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agent = PPO(n_states,n_actions,cfg)
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return env,agent
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cfg = PPOConfig()
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plot_cfg = PlotConfig()
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# 训练
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env,agent = env_agent_config(cfg,seed=1)
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rewards, ma_rewards = train(cfg, env, agent)
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make_dir(plot_cfg.result_path, plot_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=plot_cfg.model_path)
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save_results(rewards, ma_rewards, tag='train', path=plot_cfg.result_path)
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plot_rewards(rewards, ma_rewards, plot_cfg, tag="train")
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# 测试
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env,agent = env_agent_config(cfg,seed=10)
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agent.load(path=plot_cfg.model_path)
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rewards,ma_rewards = eval(cfg,env,agent)
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save_results(rewards,ma_rewards,tag='eval',path=plot_cfg.result_path)
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plot_rewards(rewards,ma_rewards,plot_cfg,tag="eval") |