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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, globimport numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport numpynumpy.random.bit_generator = numpy.random.bit_generatorimport torchim=from atari_wrappers import make_atari, wrap_deepmind, LazyFramesfrom IPython.display import clear_outputfrom tensorboardX import SummaryWriterfrom gym import envsenv_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_sizeself.next_idx = 0def push(self, state, action, reward, next_state, done):data = (state, action, reward, next_state, done)if len(self.buffer) <= self.memory_size: # buffer not fullself.buffer.append(data)else: # buffer is fullself.buffer[self.next_idx] = dataself.=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 = datastates.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), donesdef 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_spaceself.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_CUDAif 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 tensorstate = torch.from_numpy(lazyframe._force().transpose(2, 0, 1)[None] / 255).float()if self.USE_CUDA:state = state.to(device)return statedef value(self, state):q_values = self.DQN(state)return q_valuesdef act(self, state, epsilon=None):"""sample actions with epsilon-greedy policyrecap: with p = epsilon pick random action, else pick action with highest Q(s,a)"""if epsilon is None:epsilon = self.epsilonq_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 acitondef 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 statespredicted_qvalues = self.DQN(states) # [32,action]# print("predicted_qvalues:",predicted_qvalues)# input()# select q-values for chosen actionspredicted_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 statespredicted_next_qvalues = self.DQN_target(next_states)# compute V*(next_states) using predicted next q-valuesnext_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 existtarget_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 lossdef 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 = datastates.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), donesdef 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 = pute_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-v4idname = 'PongNoFrameskip-v4'env = make_atari(idname)env = wrap_deepmind(env, scale=False, frame_stack=True)#state = env.reset()#print(state.count())gamma = 0.99epsilon_max = 1epsilon_min = 0.01eps_decay = 30000frames = 2000000USE_CUDA = Truelearning_rate = 2e-4max_buff = 100000update_tar_interval = 1000batch_size = 32print_interval = 1000log_interval = 1000learning_start = 10000win_reward = 18 # Pong-v4win_break = Trueaction_space = env.action_spaceaction_dim = env.action_space.nstate_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 = 0all_rewards = []losses = []episode_num = 0is_win = False# tensorboardsummary_writer = SummaryWriter(log_dir="DQN_stackframe", comment="good_makeatari")# e-greedy decayepsilon_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_frameloss = 0if 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 = 0episode_num += 1avg_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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