153 lines
6.8 KiB
Python
153 lines
6.8 KiB
Python
from lib2to3.pytree import type_repr
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import sys
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import os
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from parso import parse
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import torch.nn as nn
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import torch.nn.functional as F
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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 datetime
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import numpy as np
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import argparse
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from common.utils import save_results_1, make_dir
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from common.utils import plot_rewards,save_args
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from dqn import DQN
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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") # Obtain current time
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parser = argparse.ArgumentParser(description="hyperparameters")
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parser.add_argument('--algo_name',default='DQN',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('--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=0.95,type=float,help="discounted factor")
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parser.add_argument('--epsilon_start',default=0.95,type=float,help="initial value of epsilon")
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parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon")
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parser.add_argument('--epsilon_decay',default=500,type=int,help="decay rate of epsilon")
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parser.add_argument('--lr',default=0.0001,type=float,help="learning rate")
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parser.add_argument('--memory_capacity',default=100000,type=int,help="memory capacity")
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parser.add_argument('--batch_size',default=64,type=int)
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parser.add_argument('--target_update',default=4,type=int)
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parser.add_argument('--hidden_dim',default=256,type=int)
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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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args.device = torch.device(
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"cuda" if torch.cuda.is_available() else "cpu") # check GPU
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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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n_actions = env.action_space.n # 动作维度
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print(f"n states: {n_states}, n actions: {n_actions}")
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agent = DQN(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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''' Training
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'''
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print('Start training!')
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print(f'Env:{cfg.env_name}, A{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 = []
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for i_ep in range(cfg.train_eps):
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ep_reward = 0 # 记录一回合内的奖励
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ep_step = 0
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state = env.reset() # 重置环境，返回初始状态
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while True:
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ep_step += 1
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action = agent.choose_action(state) # 选择动作
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next_state, reward, done, _ = env.step(action) # 更新环境，返回transition
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agent.memory.push(state, action, reward,
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next_state, done) # 保存transition
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state = next_state # 更新下一个状态
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agent.update() # 更新智能体
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ep_reward += reward # 累加奖励
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if done:
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break
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if (i_ep + 1) % cfg.target_update == 0: # 智能体目标网络更新
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agent.target_net.load_state_dict(agent.policy_net.state_dict())
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steps.append(ep_step)
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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) % 1 == 0:
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print(f'Episode：{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.2f}, Step:{ep_step:.2f} Epislon:{agent.epsilon(agent.frame_idx):.3f}')
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print('Finish training!')
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env.close()
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res_dic = {'rewards':rewards,'ma_rewards':ma_rewards,'steps':steps}
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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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############# 由于测试不需要使用epsilon-greedy策略，所以相应的值设置为0 ###############
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cfg.epsilon_start = 0.0 # e-greedy策略中初始epsilon
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cfg.epsilon_end = 0.0 # e-greedy策略中的终止epsilon
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################################################################################
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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 = []
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for i_ep in range(cfg.test_eps):
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ep_reward = 0 # 记录一回合内的奖励
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ep_step = 0
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state = env.reset() # 重置环境，返回初始状态
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while True:
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ep_step+=1
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action = agent.choose_action(state) # 选择动作
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next_state, reward, done, _ = env.step(action) # 更新环境，返回transition
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state = next_state # 更新下一个状态
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ep_reward += reward # 累加奖励
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if done:
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break
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steps.append(ep_step)
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rewards.append(ep_reward)
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if ma_rewards:
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ma_rewards.append(ma_rewards[-1] * 0.9 + ep_reward * 0.1)
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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.test_eps}, Reward:{ep_reward:.2f}, Step:{ep_step:.2f}')
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print('完成测试！')
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env.close()
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return {'rewards':rewards,'ma_rewards':ma_rewards,'steps':steps}
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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) # 创建保存结果和模型路径的文件夹
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save_args(cfg)
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agent.save(path=cfg.model_path) # 保存模型
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save_results_1(res_dic, tag='train',
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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="train") # 画出结果
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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_1(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
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