132 lines
5.7 KiB
Python
132 lines
5.7 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 gym
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import torch
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import numpy as np
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import datetime
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import argparse
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from common.utils import plot_rewards,save_args,save_results,make_dir
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from ppo2 import PPO
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def get_args():
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""" Hyperparameters
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"""
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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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parser = argparse.ArgumentParser(description="hyperparameters")
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parser.add_argument('--algo_name',default='PPO',type=str,help="name of algorithm")
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parser.add_argument('--env_name',default='CartPole-v0',type=str,help="name of environment")
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parser.add_argument('--continuous',default=False,type=bool,help="if PPO is continous") # PPO既可适用于连续动作空间，也可以适用于离散动作空间
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parser.add_argument('--train_eps',default=200,type=int,help="episodes of training")
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parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing")
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parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor")
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parser.add_argument('--batch_size',default=5,type=int) # mini-batch SGD中的批量大小
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parser.add_argument('--n_epochs',default=4,type=int)
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parser.add_argument('--actor_lr',default=0.0003,type=float,help="learning rate of actor net")
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parser.add_argument('--critic_lr',default=0.0003,type=float,help="learning rate of critic net")
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parser.add_argument('--gae_lambda',default=0.95,type=float)
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parser.add_argument('--policy_clip',default=0.2,type=float) # PPO-clip中的clip参数，一般是0.1~0.2左右
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parser.add_argument('--update_fre',default=20,type=int)
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parser.add_argument('--hidden_dim',default=256,type=int)
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parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda")
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parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
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'/' + curr_time + '/results/' )
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parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
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'/' + curr_time + '/models/' ) # path to save models
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parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")
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args = parser.parse_args()
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return args
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def env_agent_config(cfg,seed = 1):
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''' 创建环境和智能体
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'''
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env = gym.make(cfg.env_name) # 创建环境
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n_states = env.observation_space.shape[0] # 状态维度
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if cfg.continuous:
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n_actions = env.action_space.shape[0] # 动作维度
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else:
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n_actions = env.action_space.n # 动作维度
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agent = PPO(n_states, n_actions, cfg) # 创建智能体
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if seed !=0: # 设置随机种子
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torch.manual_seed(seed)
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env.seed(seed)
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np.random.seed(seed)
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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'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 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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env.close()
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res_dic = {'rewards':rewards,'ma_rewards':ma_rewards}
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return res_dic
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def test(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_name}, 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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env.close()
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res_dic = {'rewards':rewards,'ma_rewards':ma_rewards}
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return res_dic
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if __name__ == "__main__":
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cfg = get_args()
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# 训练
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env, agent = env_agent_config(cfg)
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res_dic = train(cfg, env, agent)
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make_dir(cfg.result_path, cfg.model_path)
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save_args(cfg) # 保存参数
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agent.save(path=cfg.model_path) # save model
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save_results(res_dic, tag='train',
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path=cfg.result_path)
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plot_rewards(res_dic['rewards'], res_dic['ma_rewards'], cfg, tag="train")
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# 测试
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env, agent = env_agent_config(cfg)
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agent.load(path=cfg.model_path) # 导入模型
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res_dic = test(cfg, env, agent)
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save_results(res_dic, tag='test',
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path=cfg.result_path) # 保存结果
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plot_rewards(res_dic['rewards'], res_dic['ma_rewards'],cfg, tag="test") # 画出结果 |