103 lines
3.6 KiB
Python
103 lines
3.6 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: 2020-06-11 20:58:21
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@LastEditor: John
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LastEditTime: 2021-03-20 16:58:04
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@Discription:
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@Environment: python 3.7.9
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'''
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import sys,os
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sys.path.append(os.getcwd()) # add current terminal path
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import torch
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import gym
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import datetime
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from A2C.agent import A2C
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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/"):
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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):
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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/"):
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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):
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os.mkdir(RESULT_PATH)
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class A2CConfig:
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def __init__(self):
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self.gamma = 0.99
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self.lr = 3e-4 # learnning rate
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self.actor_lr = 1e-4 # learnning rate of actor network
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self.memory_capacity = 10000 # capacity of replay memory
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self.batch_size = 128
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self.train_eps = 200
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self.train_steps = 200
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self.eval_eps = 200
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self.eval_steps = 200
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self.target_update = 4
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self.hidden_dim=256
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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def train(cfg,env,agent):
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print('Start to train ! ')
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for i_episode in range(cfg.train_eps):
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state = env.reset()
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log_probs = []
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values = []
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rewards = []
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masks = []
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entropy = 0
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ep_reward = 0
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for i_step in range(cfg.train_steps):
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state = torch.FloatTensor(state).to(cfg.device)
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dist, value = agent.model(state)
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action = dist.sample()
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next_state, reward, done, _ = env.step(action.cpu().numpy())
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ep_reward+=reward
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state = next_state
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log_prob = dist.log_prob(action)
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entropy += dist.entropy().mean()
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log_probs.append(log_prob)
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values.append(value)
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rewards.append(torch.FloatTensor(reward).unsqueeze(1).to(cfg.device))
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masks.append(torch.FloatTensor(1 - done).unsqueeze(1).to(cfg.device))
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if done:
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break
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print('Episode:{}/{}, Reward:{}, Steps:{}, Done:{}'.format(i_episode+1,cfg.train_eps,ep_reward,i_step+1,done))
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next_state = torch.FloatTensor(next_state).to(cfg.device)
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_, next_value =agent.model(next_state)
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returns = agent.compute_returns(next_value, rewards, masks)
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log_probs = torch.cat(log_probs)
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returns = torch.cat(returns).detach()
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values = torch.cat(values)
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advantage = returns - values
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actor_loss = -(log_probs * advantage.detach()).mean()
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critic_loss = advantage.pow(2).mean()
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loss = actor_loss + 0.5 * critic_loss - 0.001 * entropy
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agent.optimizer.zero_grad()
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loss.backward()
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agent.optimizer.step()
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print('Complete training！')
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if __name__ == "__main__":
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cfg = A2CConfig()
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env = gym.make('CartPole-v0')
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env.seed(1) # set random seed for env
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state_dim = env.observation_space.shape[0]
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action_dim = env.action_space.n
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agent = A2C(state_dim, action_dim, cfg)
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train(cfg,env,agent)
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