用TensorFlow2搭建DQN模型

DQN是一种深度强化学习算法，可以用于解决强化学习问题。在TensorFlow2中搭建DQN模型需要进行以下步骤：

1.导入相关的库

import tensorflow as tf
from tensorflow.keras import layers

2.构建模型

DQN模型由两个部分组成，分别是网络和损失函数。

网络部分：

class DQN(tf.keras.Model):
    def __init__(self, num_actions):
        super(DQN, self).__init__()
        self.conv1 = layers.Conv2D(32, 8, strides=4, activation='relu')
        self.conv2 = layers.Conv2D(64, 4, strides=2, activation='relu')
        self.conv3 = layers.Conv2D(64, 3, strides=1, activation='relu')
        self.flatten = layers.Flatten()
        self.dense1 = layers.Dense(512, activation='relu')
        self.dense2 = layers.Dense(num_actions)

    def call(self, inputs):
        x = self.conv1(inputs)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.flatten(x)
        x = self.dense1(x)
        x = self.dense2(x)
        return x

损失函数部分：

@tf.function
def compute_loss(model, target_model, states, actions, rewards, next_states, is_terminal, gamma):
    Q = model(states)
    Q_target = target_model(next_states)
    Q_target = tf.stop_gradient(Q_target)
    max_Q = tf.reduce_max(Q_target, axis=1)
    target_Q = rewards + (1 - is_terminal) * gamma * max_Q
    action_masks = tf.one_hot(actions, num_actions)
    Q_action = tf.reduce_sum(tf.multiply(Q, action_masks), axis=1)
    loss = tf.reduce_mean(tf.square(target_Q - Q_action))
    return loss

3.定义优化器

optimizer = tf.optimizers.Adam(learning_rate=learning_rate)

4.定义重放缓冲区

class ReplayBuffer(object):
    def __init__(self, buffer_size):
        self.buffer_size = buffer_size
        self.num_experiences = 0
        self.buffer = []

    def get_batch(self, batch_size):
        if self.num_experiences < batch_size:
            return None
        else:
            indices = np.random.choice(self.num_experiences, size=batch_size, replace=False)
            states_batch      = np.array([self.buffer[i][0] for i in indices])
            actions_batch     = np.array([self.buffer[i][1] for i in indices])
            rewards_batch     = np.array([self.buffer[i][2] for i in indices])
            next_states_batch = np.array([self.buffer[i][3] for i in indices])
            is_terminal_batch = np.array([self.buffer[i][4] for i in indices])
            return states_batch, actions_batch, rewards_batch, next_states_batch, is_terminal_batch

    def size(self):
        return self.buffer_size

    def add(self, state, action, reward, next_state, is_terminal):
        experience = (state, action, reward, next_state, is_terminal)
        if self.num_experiences < self.buffer_size:
            self.buffer.append(experience)
            self.num_experiences += 1
        else:
            self.buffer.pop(0)
            self.buffer.append(experience)

5.训练模型

def train(model, target_model, optimizer, replay_buffer, gamma, batch_size):
    states_batch, action_batch, reward_batch, next_states_batch, is_terminal_batch = replay_buffer.get_batch(batch_size)
    loss = compute_loss(model, target_model, states_batch, action_batch, reward_batch, next_states_batch, is_terminal_batch, gamma)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    return loss

6.训练过程

for episode in range(max_episodes):
    state = env.reset()
    done = False
    total_reward = 0
    while not done:
        action = choose_action(model, state, num_actions, epsilon)
        next_state, reward, done, info = env.step(action)
        total_reward += reward
        replay_buffer.add(state, action, reward, next_state, done)
        state = next_state
        if replay_buffer.size() > batch_size:
            loss = train(model, target_model, optimizer, replay_buffer, gamma, batch_size)
        if num_steps % update_target_model_freq == 0:
            update_target_model(model, target_model)
        if num_steps % epsilon_decay_steps == 0:
            epsilon = max(epsilon * epsilon_decay, epsilon_min)
        num_steps += 1
    print("Episode:", episode, "Total Reward:", total_reward)

以上就是用TensorFlow2搭建DQN模型的完整流程，其中需要注意的是，我们在实现时还需实现epsilon-greedy策略和目标网络更新等操作。