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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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