187 lines
7.5 KiB
Python
187 lines
7.5 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: 2020-06-11 20:58:21
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@LastEditor: John
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LastEditTime: 2020-11-08 22:19:56
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@Discription:
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@Environment: python 3.7.9
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'''
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import torch
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import gym
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import os
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import numpy as np
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import argparse
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from torch.utils.tensorboard import SummaryWriter
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from agent import A2C
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from env import make_envs
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from utils import SEQUENCE, SAVED_MODEL_PATH, RESULT_PATH
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from utils import save_model,save_results
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def get_args():
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'''模型建立好之后只需要在这里调参
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'''
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parser = argparse.ArgumentParser()
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parser.add_argument("--train", default=1, type=int) # 1 表示训练，0表示只进行eval
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parser.add_argument("--gamma", default=0.99,
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type=float) # reward 折扣因子
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parser.add_argument("--lr", default=3e-4, type=float) # critic学习率
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parser.add_argument("--actor_lr", default=1e-4, type=float)
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parser.add_argument("--memory_capacity", default=10000,
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type=int, help="capacity of Replay Memory")
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parser.add_argument("--batch_size", default=128, type=int,
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help="batch size of memory sampling")
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parser.add_argument("--train_eps", default=4000, type=int)
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parser.add_argument("--train_steps", default=5, type=int)
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parser.add_argument("--eval_eps", default=200, type=int) # 训练的最大episode数目
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parser.add_argument("--eval_steps", default=200,
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type=int) # 训练每个episode的长度
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parser.add_argument("--target_update", default=4, type=int,
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help="when(every default 10 eisodes) to update target net ")
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config = parser.parse_args()
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return config
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def test_env(agent,device='cpu'):
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env = gym.make("CartPole-v0")
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state = env.reset()
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ep_reward=0
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for _ in range(200):
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state = torch.FloatTensor(state).unsqueeze(0).to(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()[0])
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state = next_state
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ep_reward += reward
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if done:
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break
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return ep_reward
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def train(cfg):
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print('Start to train ! \n')
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envs = make_envs(num_envs=16,env_name="CartPole-v0")
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n_states = envs.observation_space.shape[0]
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n_actions = envs.action_space.n
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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agent = A2C(n_states, n_actions, hidden_dim=256)
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# moving_average_rewards = []
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# ep_steps = []
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log_dir=os.path.split(os.path.abspath(__file__))[0]+"/logs/train/" + SEQUENCE
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writer = SummaryWriter(log_dir)
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state = envs.reset()
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for i_episode in range(1, cfg.train_eps+1):
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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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for i_step in range(1, cfg.train_steps+1):
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state = torch.FloatTensor(state).to(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, _ = envs.step(action.cpu().numpy())
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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(device))
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masks.append(torch.FloatTensor(1 - done).unsqueeze(1).to(device))
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if i_episode%20 == 0:
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print("reward",test_env(agent,device='cpu'))
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next_state = torch.FloatTensor(next_state).to(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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for _ in range(100):
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print("test_reward",test_env(agent,device='cpu'))
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# print('Episode:', i_episode, ' Reward: %i' %
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# int(ep_reward[0]), 'n_steps:', i_step)
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# ep_steps.append(i_step)
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# rewards.append(ep_reward)
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# if i_episode == 1:
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# moving_average_rewards.append(ep_reward[0])
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# else:
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# moving_average_rewards.append(
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# 0.9*moving_average_rewards[-1]+0.1*ep_reward[0])
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# writer.add_scalars('rewards',{'raw':rewards[-1], 'moving_average': moving_average_rewards[-1]}, i_episode)
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# writer.add_scalar('steps_of_each_episode',
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# ep_steps[-1], i_episode)
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writer.close()
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print('Complete training！')
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''' 保存模型 '''
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# save_model(agent,model_path=SAVED_MODEL_PATH)
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# '''存储reward等相关结果'''
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# save_results(rewards,moving_average_rewards,ep_steps,tag='train',result_path=RESULT_PATH)
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# def eval(cfg, saved_model_path = SAVED_MODEL_PATH):
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# print('start to eval ! \n')
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# env = NormalizedActions(gym.make("Pendulum-v0"))
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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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# agent = DDPG(n_states, n_actions, critic_lr=1e-3,
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# actor_lr=1e-4, gamma=0.99, soft_tau=1e-2, memory_capacity=100000, batch_size=128)
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# agent.load_model(saved_model_path+'checkpoint.pth')
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# rewards = []
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# moving_average_rewards = []
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# ep_steps = []
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# log_dir=os.path.split(os.path.abspath(__file__))[0]+"/logs/eval/" + SEQUENCE
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# writer = SummaryWriter(log_dir)
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# for i_episode in range(1, cfg.eval_eps+1):
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# state = env.reset() # reset环境状态
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# ep_reward = 0
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# for i_step in range(1, cfg.eval_steps+1):
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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) # 更新环境参数
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# ep_reward += reward
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# state = next_state # 跳转到下一个状态
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# if done:
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# break
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# print('Episode:', i_episode, ' Reward: %i' %
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# int(ep_reward), 'n_steps:', i_step, 'done: ', done)
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# ep_steps.append(i_step)
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# rewards.append(ep_reward)
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# # 计算滑动窗口的reward
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# if i_episode == 1:
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# moving_average_rewards.append(ep_reward)
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# else:
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# moving_average_rewards.append(
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# 0.9*moving_average_rewards[-1]+0.1*ep_reward)
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# writer.add_scalars('rewards',{'raw':rewards[-1], 'moving_average': moving_average_rewards[-1]}, i_episode)
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# writer.add_scalar('steps_of_each_episode',
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# ep_steps[-1], i_episode)
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# writer.close()
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# '''存储reward等相关结果'''
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# if not os.path.exists(RESULT_PATH): # 检测是否存在文件夹
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# os.mkdir(RESULT_PATH)
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# np.save(RESULT_PATH+'rewards_eval.npy', rewards)
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# np.save(RESULT_PATH+'moving_average_rewards_eval.npy', moving_average_rewards)
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# np.save(RESULT_PATH+'steps_eval.npy', ep_steps)
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if __name__ == "__main__":
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cfg = get_args()
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train(cfg)
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# cfg = get_args()
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# if cfg.train:
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# train(cfg)
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# eval(cfg)
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# else:
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# model_path = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"
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# eval(cfg,saved_model_path=model_path)
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