深度强化学习中深度Q网络（Q-Learning+CNN）的讲解以及在Atari游戏中的实战（超详细 附源码）_深度q网络算法

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深度强化学习将深度学习的感知（预测能力）与强化学习的决策能力相结合，利用深度神经网络具有有效识别高维数据的能力，使得强化学习算法在处理高纬度状态空间任务中更加有效

一、DQN算法简介

1：核心思想

深度Q网络算法（DQN）是一种经典的基于值函数的深度强化学习算法，它将卷积神经网络与Q-Learning算法相结合，利用CNN对图像的强大表征能力，将视频帧视为强化学习中的状态输入网络，然后由网络输出离散的动作值函数，Agent再根据动作值函数选择对应的动作

DQN利用CNN输入原始图像数据，能够在不依赖于任意特定问题的情况下，采用相同的算法模型，在广泛的问题中获得较好的学习效果，常用于处理Atari游戏

2：模型架构

深度Q网络模型架构的输入是距离当前时刻最近的连续4帧预处理后的图像，该输入信号经过3哥卷积层和2个全连接层的非线性变换，变换成低维的，抽象的特征表达，并最终在输出层产生每个动作对应的Q值函数

具体架构如下

1：输入层

2：对输入层进行卷积操作

3：对第一隐藏层的输出进行卷积操作

4：对第二隐藏层的输出进行卷积操作

5：第三隐藏层与第四隐藏层的全连接操作

6：第四隐藏层与输出层的全连接操作

3：数据预处理 

包括以下几个部分

1：图像处理

2：动态信息预处理

3：游戏得分预处理

4：游戏随机开始的预处理

二、训练算法 

 DQN之所以能够较好的将深度学习与强化学习相结合，是因为它引入了三个核心技术 

1：目标函数

使用卷积神经网络结合全连接作为动作值函数的逼近器，实现端到端的效果，输入为视频画面，输出为有限数量的动作值函数

2：目标网络

设置目标网络来单独处理TD误差 使得目标值相对稳定

3：经验回放机制

有效解决数据间的相关性和非静态问题，使得网络输入的信息满足独立同分布的条件

 DQN训练流程图如下

 三、DQN算法优缺点

DQN算法的优点在于：算法通用性强，是一种端到端的处理方式，可为监督学习产生大量的样本。其缺点在于：无法应用于连续动作控制，只能处理具有短时记忆的问题，无法处理需长时记忆的问题，且算法不一定收敛，需要仔细调参

四、DQN在Breakout、Asterix游戏中的实战

接下来通过Atari 2600游戏任务中的Breakout，Asterix游戏来验证DQN算法的性能。

在训练过程中 Agent实行贪心策略，开始值为1并与环境进行交互，并将交互的样本经验保存在经验池中，点对于每个Atari游戏，DQN算法训练1000000时间步，每经历10000时间步，Agent将行为网络的参数复杂到目标网络，每经历1000时间步，模型进行一次策略性能评估

可视化如下 

训练阶段的实验数据如下

可以看出 有固定目标值的Q网络可以提高训练的稳定性和收敛性

loss变化如下 

 五、代码

部分代码如下

 
import gym, random, pickle, os.path, math, glob
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import numpy
numpy.random.bit_generator = numpy.random.bit_generator
import torch
im=
from atari_wrappers import make_atari, wrap_deepmind, LazyFrames
from IPython.display import clear_output
from tensorboardX import SummaryWriter
from gym import envs
env_names = [spec for spec in envs.registry]
for name in sorted(env_names):
	print(name)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 
class DQN(nn.Module):
    def __init__(self, in_channels=4, num_actions=5):
 
= nn.Conv2d(32, 64, kernel_size=4, stride=2)
        self.conv3 = nn.Conv2d(64, 64, kernel_size=3, stride=1)
        self.fc4 = nn.Linear(7 * 7 * 64, 512)
        self.fc5 = nn.Linear(512, num_actions)
 
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        x = F.relu(self.fc4(x.view(x.size(0), -1)))  # 输出的维度是为[x.size(0),1]
        return self.fc5(x)
 
 
class Memory_Buffer(object):
    def __init__(self, memory_size=1000):
        self.buffer = []
        self.memory_size = memory_size
        self.next_idx = 0
 
    def push(self, state, action, reward, next_state, done):
        data = (state, action, reward, next_state, done)
        if len(self.buffer) <= self.memory_size:  # buffer not full
            self.buffer.append(data)
        else:  # buffer is full
            self.buffer[self.next_idx] = data
        self.=s, rewards, next_states, dones = [], [], [], [], []
        for i in range(batch_size):
            idx = random.randint(0, self.size() - 1)
            data = self.buffer[idx]
            state, action, reward, next_state, done = data
            states.append(state)
            actions.append(action)
            rewards.append(reward)
            next_states.append(next_state)
            dones.append(done)
 
        return np.concatenate(states), actions, rewards, np.concatenate(next_states), dones
 
    def size(self):
        return len(self.buffer)
 
 
class DQNAgent:
    def __init__(self, in_channels=1, action_space=[], USE_CUDA=False, memory_size=10000, epsilon=1, lr=1e-4):
        self.epsilo=ction_space
        self.memory_buffer = Memory_Buffer(memory_size)
        self.DQN = DQN(in_channels=in_channels, num_actions=action_space.n)
        self.DQN_target = DQN(in_channels=in_channels, num_actions=action_space.n)
        self.DQN_target.load_state_dict(self.DQN.state_dict())
 
        self.USE_CUDA = USE_CUDA
        if USE_CUDA:
            self.DQN = self.DQN.to(device)
            self.DQN_target = self.DQN_target.to(device)
        self.optimizer = optim.RMSprop(self.DQN.parameters(), lr=lr, eps=0.001, alpha=0.95)
 
    def observe(self, lazyframe):
        # from Lazy frame to tensor
        state = torch.from_numpy(lazyframe._force().transpose(2, 0, 1)[None] / 255).float()
        if self.USE_CUDA:
            state = state.to(device)
        return state
 
    def value(self, state):
        q_values = self.DQN(state)
        return q_values
 
    def act(self, state, epsilon=None):
        """
        sample actions with epsilon-greedy policy
        recap: with p = epsilon pick random action, else pick action with highest Q(s,a)
        """
        if epsilon is None:
            epsilon = self.epsilon
 
        q_values = self.value(state).cpu().detach().numpy()
        if random.random() < epsilon:
            aciton = random.randrange(self.action_space.n)
        else:
            aciton = q_values.argmax(1)[0]
        return aciton
 
    def compute_td_loss(self, states, actions, rewards, next_states, is_done, gamma=0=tensor(actions).long()  # shape: [batch_size]
        rewards = torch.tensor(rewards, dtype=torch.float)  # shape: [batch_size]
        is_done = torch.tensor(is_done, dtype=torch.uint8)  # shape: [batch_size]
 
        if self.USE_CUDA:
            actions = actions.to(device)
            rewards = rewards.to(device)
            is_done = is_done.to(device)
 
        # get q-values for all actions in current states
        predicted_qvalues = self.DQN(states)  # [32,action]
        #         print("predicted_qvalues:",predicted_qvalues)
        #         input()
        # select q-values for chosen actions
        predicted_qvalues_for_actions = predicted_qvalues[range(states.shape[0]), actions]
        #         print("predicted_qvalues_for_actions:",predicted_qvalues_for_actions)
        #         input()
        # compute q-values for all actions in next states
        predicted_next_qvalues = self.DQN_target(next_states)
 
        # compute V*(next_states) using predicted next q-values
        next_state_values = predicted_next_qvalues.max(-1)[0]
 
        # compute "target q-values" for loss - it's what's inside square parentheses in the above formula.
        target_qvalues_for_actions = rewards + gamma * next_state_values
 
        # at the last state we shall use simplified formula: Q(s,a) = r(s,a) since s' doesn't exist
        target_qvalues_for_actions = torch.where(is_done, rewards, target_qvalues_for_actions)
 
        # mean squared error loss to minimize
        # loss = torch.mean((predicted_qvalues_for_actions -
        #                   target_qvalues_for_actions.detach()) ** 2)
        loss = F.smooth_l1_loss(predicted_qvalues_for_actions, target_qvalues_for_actions.detach())
 
        return loss
 
    def sample_from_buffer(self, batch_size):
        states, actions, rewards, next_states, dones = [], [], [], [], []
        for i in range(batch_size):
            idx = random.randint(0, self.memory_buffer.size() - 1)
            data = self.memory_buffer.buffer[idx]
            frame, action, reward, next_frame, done = data
            states.append(self.observe(frame))
            actions.append(action)
            rewards.append(reward)
            next_states.append(self.observe(next_frame))
            dones.append(done)
        return torch.cat(states), actions, rewards, torch.cat(next_states), dones
 
    def learn_from_experience(self, batch_size):
        if self.memory_buffer.size() > batch_size:
            states, actions, rewards, next_states, dones = self.sample_from_buffer(batch_size)
            td_loss = self.compute_td_loss(states, actions, rewards, next_states, dones)
            self.optimizer.zero_grad()
            td_loss.backward()
            for param in self.DQN.parameters():
                param.grad.data.clamp_(-1, 1)  # 梯度截断，防止梯度爆炸
 
            self.optimizer.step()
            return (td_loss.item())
        else:
            return (0)
 
 
def plot_training(frame_idx, rewards, losses):
    pd.DataFrame(rewards, columns=['Reward']).to_csv(idname, index=False)
    clear_output(True)
    plt.figure(figsize=(20, 5))
    plt.subplot(131)
    plt.title('frame %s. reward: %s' % (frame_idx, np.mean(rewards[-10:])))
    plt.plot(rewards)
    plt.subplot(132)
    plt.title('loss')
    plt.plot(losses)
    plt.show()
 
 
# Training DQN in PongNoFrameskip-v4
idname = 'PongNoFrameskip-v4'
env = make_atari(idname)
env = wrap_deepmind(env, scale=False, frame_stack=True)
 
#state = env.reset()
#print(state.count())
gamma = 0.99
epsilon_max = 1
epsilon_min = 0.01
eps_decay = 30000
frames = 2000000
USE_CUDA = True
learning_rate = 2e-4
max_buff = 100000
update_tar_interval = 1000
batch_size = 32
print_interval = 1000
log_interval = 1000
learning_start = 10000
win_reward = 18  # Pong-v4
win_break = True
 
action_space = env.action_space
action_dim = env.action_space.n
state_dim = env.observation_space.shape[0]
state_channel = env.observation_space.shape[2]
 
agent = DQNAgent(in_channels=state_channel, action_space=action_space, USE_CUDA=USE_CUDA, lr=learning_rate)
 
#frame = env.reset()
 
 
episode_reward = 0
all_rewards = []
losses = []
episode_num = 0
is_win = False
# tensorboard
summary_writer = SummaryWriter(log_dir="DQN_stackframe", comment="good_makeatari")
 
# e-greedy decay
epsilon_by_frame = lambda frame_idx: epsilon_min + (epsilon_max - epsilon_min) * math.exp(-1. * frame_idx / eps_decay)
plt.plot([epsilon_by_frame(i) for i in range(10000)])
 
for i in range(frames):
    epsilon = epsilon_by_frame(i)
    #state_tensor = agent.observe(frames)
    #action = agent.act(state_tensor, epsilon)
 
    #next_frame, reward, done, _ = env.step(action)
 
    #episode_reward += reward
    #agent.memory_buffer.push(frame, action, reward, next_frame, done)
    #frame = next_frame
 
    loss = 0
    if agent.memory_buffer.size() >= learning_start:
        loss = agent.learn_from_experience(batch_size)
        losses.append(loss)
 
    if i % print_interval == 0:
        print("frames: %5d, reward: %5f, loss: %4f, epsilon: %5f, episode: %4d" %
              (i, np.mean(all_rewards[-10:]), loss, epsilon, episode_num))
        summary_writer.add_scalar("Temporal Difference Loss", loss, i)
        summary_writer.add_scalar("Mean Reward", np.mean(all_rewards[-10:]), i)
        summary_writer.add_scalar("Epsilon", epsilon, i)
 
    if i % update_tar_interval == 0:
        agent.DQN_target.load_state_dict(agent.DQN.state_dict())
    '''
    if done:
        frame = env.reset()
        all_rewards.append(episode_reward)
        episode_reward = 0
        episode_num += 1
        avg_reward = float(np.mean(all_rewards[-100:]))
    '''
summary_writer.close()
# 保存网络参数
#torch.save(agent.DQN.state_dict(), "trained model/DQN_dict.pth.tar")
plot_training(i, all_r=

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