178 lines
7.4 KiB
Python
178 lines
7.4 KiB
Python
import sys
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import os
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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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from common.utils import save_results_1, make_dir
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from common.utils import plot_rewards
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from dqn import DQN
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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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class MLP(nn.Module):
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def __init__(self, n_states,n_actions,hidden_dim=128):
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""" 初始化q网络，为全连接网络
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n_states: 输入的特征数即环境的状态维度
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n_actions: 输出的动作维度
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"""
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super(MLP, self).__init__()
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self.fc1 = nn.Linear(n_states, hidden_dim) # 输入层
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self.fc2 = nn.Linear(hidden_dim,hidden_dim) # 隐藏层
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self.fc3 = nn.Linear(hidden_dim, n_actions) # 输出层
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def forward(self, x):
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# 各层对应的激活函数
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x = F.relu(self.fc1(x))
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x = F.relu(self.fc2(x))
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return self.fc3(x)
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class Config:
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'''超参数
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'''
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def __init__(self):
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############################### hyperparameters ################################
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self.algo_name = 'DQN' # algorithm name
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self.env_name = 'CartPole-v0' # environment name
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self.device = torch.device(
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"cuda" if torch.cuda.is_available() else "cpu") # check GPU
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self.seed = 10 # 随机种子，置0则不设置随机种子
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self.train_eps = 200 # 训练的回合数
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self.test_eps = 20 # 测试的回合数
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################################################################################
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################################## 算法超参数 ###################################
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self.gamma = 0.95 # 强化学习中的折扣因子
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self.epsilon_start = 0.90 # e-greedy策略中初始epsilon
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self.epsilon_end = 0.01 # e-greedy策略中的终止epsilon
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self.epsilon_decay = 500 # e-greedy策略中epsilon的衰减率
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self.lr = 0.0001 # 学习率
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self.memory_capacity = 100000 # 经验回放的容量
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self.batch_size = 64 # mini-batch SGD中的批量大小
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self.target_update = 4 # 目标网络的更新频率
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self.hidden_dim = 256 # 网络隐藏层
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################################################################################
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################################# 保存结果相关参数 ################################
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self.result_path = curr_path + "/outputs/" + self.env_name + \
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'/' + curr_time + '/results/' # 保存结果的路径
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self.model_path = curr_path + "/outputs/" + self.env_name + \
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'/' + curr_time + '/models/' # 保存模型的路径
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self.save = True # 是否保存图片
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################################################################################
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def env_agent_config(cfg):
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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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model = MLP(n_states,n_actions)
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agent = DQN(n_actions, model, cfg) # 创建智能体
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if cfg.seed !=0: # 设置随机种子
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torch.manual_seed(cfg.seed)
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env.seed(cfg.seed)
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np.random.seed(cfg.seed)
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return env, agent
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def train(cfg, env, agent):
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''' 训练
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'''
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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 = []
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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.test_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.train_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 = Config()
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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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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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