123 lines
4.3 KiB
Python
123 lines
4.3 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 torch
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import gym
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import numpy as np
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import datetime
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from TD3.agent import TD3
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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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curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
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class TD3Config:
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def __init__(self) -> None:
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self.algo = 'TD3' # 算法名称
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self.env_name = 'Pendulum-v1' # 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.train_eps = 600 # 训练的回合数
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self.start_timestep = 25e3 # Time steps initial random policy is used
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self.epsilon_start = 50 # Episodes initial random policy is used
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self.eval_freq = 10 # How often (episodes) we evaluate
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self.max_timestep = 100000 # Max time steps to run environment
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self.expl_noise = 0.1 # Std of Gaussian exploration noise
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self.batch_size = 256 # Batch size for both actor and critic
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self.gamma = 0.9 # gamma factor
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self.lr = 0.0005 # 学习率
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self.policy_noise = 0.2 # Noise added to target policy during critic update
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self.noise_clip = 0.3 # Range to clip target policy noise
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self.policy_freq = 2 # Frequency of delayed policy updates
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class PlotConfig(TD3Config):
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def __init__(self) -> None:
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super().__init__()
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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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# Runs policy for X episodes and returns average reward
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# A fixed seed is used for the eval environment
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def eval(env,agent, seed, eval_episodes=10):
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eval_env = gym.make(env)
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eval_env.seed(seed + 100)
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avg_reward = 0.
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for _ in range(eval_episodes):
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state, done = eval_env.reset(), False
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while not done:
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# eval_env.render()
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action = agent.choose_action(np.array(state))
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state, reward, done, _ = eval_env.step(action)
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avg_reward += reward
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avg_reward /= eval_episodes
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print("---------------------------------------")
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print(f"Evaluation over {eval_episodes} episodes: {avg_reward:.3f}")
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print("---------------------------------------")
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return avg_reward
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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}, 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(int(cfg.train_eps)):
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ep_reward = 0
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ep_timesteps = 0
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state, done = env.reset(), False
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while not done:
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ep_timesteps += 1
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# Select action randomly or according to policy
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if i_ep < cfg.epsilon_start:
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action = env.action_space.sample()
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else:
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action = (
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agent.choose_action(np.array(state))
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+ np.random.normal(0, max_action * cfg.expl_noise, size=n_actions)
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).clip(-max_action, max_action)
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# Perform action
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next_state, reward, done, _ = env.step(action)
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done_bool = float(done) if ep_timesteps < env._max_episode_steps else 0
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# Store data in replay buffer
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agent.memory.push(state, action, next_state, reward, done_bool)
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state = next_state
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ep_reward += reward
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# Train agent after collecting sufficient data
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if i_ep+1 >= cfg.epsilon_start:
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agent.update()
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if (i_ep+1)%10 == 0:
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print('回合：{}/{}, 奖励：{:.2f}'.format(i_ep+1, cfg.train_eps, ep_reward))
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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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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 = TD3Config()
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plot_cfg = PlotConfig()
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env = gym.make(cfg.env_name)
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env.seed(1) # 随机种子
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torch.manual_seed(1)
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np.random.seed(1)
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n_states = env.observation_space.shape[0]
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n_actions = env.action_space.shape[0]
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max_action = float(env.action_space.high[0])
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agent = TD3(n_states,n_actions,max_action,cfg)
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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)
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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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