123 lines
4.1 KiB
Python
123 lines
4.1 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: 2021-03-22 16:18:10
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LastEditor: John
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LastEditTime: 2021-09-26 22:05:00
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Discription:
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Environment:
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'''
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import sys,os
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curr_path = os.path.dirname(__file__)
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parent_path=os.path.dirname(curr_path)
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sys.path.append(parent_path) # add current terminal path to sys.path
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import gym
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import torch
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import datetime
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import tqdm
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from PPO.agent import PPO
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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") # obtain current time
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class PPOConfig:
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def __init__(self) -> None:
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self.env = 'CartPole-v0'
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self.algo = 'PPO'
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self.result_path = curr_path+"/results/" +self.env+'/'+curr_time+'/results/' # path to save results
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self.model_path = curr_path+"/results/" +self.env+'/'+curr_time+'/models/' # path to save models
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self.train_eps = 200 # max training episodes
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self.eval_eps = 50
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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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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # check gpu
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def env_agent_config(cfg,seed=1):
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env = gym.make(cfg.env)
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env.seed(seed)
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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 = PPO(state_dim,action_dim,cfg)
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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'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
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rewards= []
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ma_rewards = [] # moving average rewards
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running_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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running_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 running_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(
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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(f"回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward:.2f}")
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print('Complete training！')
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return rewards,ma_rewards
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def eval(cfg,env,agent):
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print('Start to eval !')
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print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
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rewards= []
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ma_rewards = [] # moving average rewards
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for i_ep in range(cfg.eval_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(f"Episode:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.3f}")
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print('Complete evaling！')
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return rewards,ma_rewards
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if __name__ == '__main__':
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cfg = PPOConfig()
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# train
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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(cfg.result_path, cfg.model_path)
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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, tag="train",
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algo=cfg.algo, path=cfg.result_path)
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# eval
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env,agent = env_agent_config(cfg,seed=10)
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agent.load(path=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=cfg.result_path)
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plot_rewards(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
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