手写DQN

引用库文件

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import random
import math
import numpy as np

创建神经网络

神经网络类以及初始化

初始化的输入是状态维度，动作维度，以及隐藏层。

class MLP(nn.Module):
    def __init__(self, state_dim,action_dim,hidden_dim=128):
        """ 初始化q网络，为全连接网络
            state_dim: 输入的特征数即环境的状态维度
            action_dim: 输出的动作维度
        """
        super(MLP, self).__init__()
        self.fc1 = nn.Linear(state_dim, hidden_dim) # 输入层
        self.fc2 = nn.Linear(hidden_dim,hidden_dim) # 隐藏层
        self.fc3 = nn.Linear(hidden_dim, action_dim) # 输出层

前向传播函数

def forward(self, x):
    # 各层对应的激活函数
    x = F.relu(self.fc1(x)) 
    x = F.relu(self.fc2(x))
    return self.fc3(x)

创建缓冲区

定义缓冲区类

class ReplayBuffer:
    def __init__(self, capacity):
        self.capacity = capacity # 经验回放的容量
        self.buffer = [] # 缓冲区
        self.position = 0 

定义push函数

def push(self, state, action, reward, next_state, done):
    ''' 缓冲区是一个队列，容量超出时去掉开始存入的转移(transition)
    '''
    if len(self.buffer) < self.capacity:
        self.buffer.append(None)
    self.buffer[self.position] = (state, action, reward, next_state, done)
    self.position = (self.position + 1) % self.capacity 

定义采样函数

def sample(self, batch_size):
    batch = random.sample(self.buffer, batch_size) # 随机采出小批量转移
    state, action, reward, next_state, done =  zip(*batch) # 解压成状态，动作等
    return state, action, reward, next_state, done

创建DQN

DQN类的创建以及初始化

定义动作维度，状态维度，以及优化器，目标网络，

class DQN:
    def __init__(self, state_dim, action_dim, cfg):
        self.action_dim = action_dim  # 总的动作个数
        self.device = cfg.device  # 设备，cpu或gpu等
        self.gamma = cfg.gamma  # 奖励的折扣因子
        # e-greedy策略相关参数
        self.frame_idx = 0  # 用于epsilon的衰减计数
        self.epsilon = lambda frame_idx: cfg.epsilon_end + \
            (cfg.epsilon_start - cfg.epsilon_end) * \
            math.exp(-1. * frame_idx / cfg.epsilon_decay)
        self.batch_size = cfg.batch_size
        self.policy_net = MLP(state_dim, action_dim,hidden_dim=cfg.hidden_dim).to(self.device)
        self.target_net = MLP(state_dim, action_dim,hidden_dim=cfg.hidden_dim).to(self.device)
        for target_param, param in zip(self.target_net.parameters(),self.policy_net.parameters()): # 复制参数到目标网路targe_net
            target_param.data.copy_(param.data)
        self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr) # 优化器
        self.memory = ReplayBuffer(cfg.memory_capacity) # 经验回放

创建动作选择函数

注意转变为tensor，再放入网络，以及取max

def choose_action(self, state):
    ''' 选择动作
    '''
    self.frame_idx += 1
    if random.random() > self.epsilon(self.frame_idx):
        with torch.no_grad():
            state = torch.tensor([state], device=self.device, dtype=torch.float32)
            q_values = self.policy_net(state)
            action = q_values.max(1)[1].item() # 选择Q值最大的动作
    else:
        action = random.randrange(self.action_dim)
    return action

网络更新函数

def update(self):
    if len(self.memory) < self.batch_size: # 当memory中不满足一个批量时，不更新策略
        return
    # 从经验回放中(replay memory)中随机采样一个批量的转移(transition)
    state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(
        self.batch_size)
    # 转为张量
    state_batch = torch.tensor(state_batch, device=self.device, dtype=torch.float)
    action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(1)  
    reward_batch = torch.tensor(reward_batch, device=self.device, dtype=torch.float)  
    next_state_batch = torch.tensor(next_state_batch, device=self.device, dtype=torch.float)
    done_batch = torch.tensor(np.float32(done_batch), device=self.device)
    q_values = self.policy_net(state_batch).gather(dim=1, index=action_batch) # 计算当前状态(s_t,a)对应的Q(s_t, a)
    next_q_values = self.target_net(next_state_batch).max(1)[0].detach() # 计算下一时刻的状态(s_t_,a)对应的Q值
    # 计算期望的Q值，对于终止状态，此时done_batch[0]=1, 对应的expected_q_value等于reward
    expected_q_values = reward_batch + self.gamma * next_q_values * (1-done_batch)
    loss = nn.MSELoss()(q_values, expected_q_values.unsqueeze(1))  # 计算均方根损失
    # 优化更新模型
    self.optimizer.zero_grad()  
    loss.backward()
    for param in self.policy_net.parameters():  # clip防止梯度爆炸
        param.grad.data.clamp_(-1, 1)
    self.optimizer.step() 

main文件

库文件导入

import gym
import torch
from dqn import DQN

config类

class Config:
    '''超参数
    '''

    def __init__(self):
        ################################## 环境超参数 ###################################
        self.algo_name = 'DQN'  # 算法名称
        self.env_name = 'CartPole-v0'  # 环境名称
        self.device = torch.device(
            "cuda" if torch.cuda.is_available() else "cpu")  # 检测GPUgjgjlkhfsf风刀霜的撒发十
        self.seed = 10 # 随机种子，置0则不设置随机种子
        self.train_eps = 200  # 训练的回合数
        self.test_eps = 30  # 测试的回合数
        ################################################################################
        
        ################################## 算法超参数 ###################################
        self.gamma = 0.95  # 强化学习中的折扣因子
        self.epsilon_start = 0.90  # e-greedy策略中初始epsilon
        self.epsilon_end = 0.01  # e-greedy策略中的终止epsilon
        self.epsilon_decay = 500  # e-greedy策略中epsilon的衰减率
        self.lr = 0.0001  # 学习率
        self.memory_capacity = 100000  # 经验回放的容量
        self.batch_size = 64  # mini-batch SGD中的批量大小
        self.target_update = 4  # 目标网络的更新频率
        self.hidden_dim = 256  # 网络隐藏层
        ################################################################################
        
        ################################# 保存结果相关参数 ################################
        self.result_path = curr_path + "/outputs/" + self.env_name + \
            '/' + curr_time + '/results/'  # 保存结果的路径
        self.model_path = curr_path + "/outputs/" + self.env_name + \
            '/' + curr_time + '/models/'  # 保存模型的路径
        self.save = True # 是否保存图片
        ################################################################################

创建环境和智能体函数

def env_agent_config(cfg):
    ''' 创建环境和智能体
    '''
    env = gym.make(cfg.env_name)  # 创建环境
    state_dim = env.observation_space.shape[0]  # 状态维度
    action_dim = env.action_space.n  # 动作维度
    agent = DQN(state_dim, action_dim, cfg)  # 创建智能体
    if cfg.seed !=0: # 设置随机种子
        torch.manual_seed(cfg.seed)
        env.seed(cfg.seed)
        np.random.seed(cfg.seed)
    return env, agent

训练函数

def train(cfg, env, agent):
    '''
    训练
    '''
    print('开始训练!')
    print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')
    rewards = []  # 记录所有回合的奖励
    ma_rewards = []  # 记录所有回合的滑动平均奖励
    for i_ep in range(cfg.train_eps):
        ep_reward = 0  # 记录一回合内的奖励
        state = env.reset()  # 重置环境，返回初始状态
        while True:
            action = agent.choose_action(state)  # 选择动作
            next_state, reward, done, _ = env.step(action)  # 更新环境，返回transition
            agent.memory.push(state, action, reward,
                              next_state, done)  # 保存transition
            state = next_state  # 更新下一个状态
            agent.update()  # 更新智能体
            ep_reward += reward  # 累加奖励
            if done:
                break
        if (i_ep + 1) % cfg.target_update == 0:  # 智能体目标网络更新
            agent.target_net.load_state_dict(agent.policy_net.state_dict())
    print('完成训练！')
    env.close()
    return rewards, ma_rewards

main函数

if __name__ == "__main__":
    cfg = Config()
    # 训练
    env, agent = env_agent_config(cfg)
    rewards, ma_rewards = train(cfg, env, agent)
    make_dir(cfg.result_path, cfg.model_path)  # 创建保存结果和模型路径的文件夹
    agent.save(path=cfg.model_path)  # 保存模型
    save_results(rewards, ma_rewards, tag='train',
                 path=cfg.result_path)  # 保存结果
    plot_rewards(rewards, ma_rewards, cfg, tag="train")  # 画出结果