dqn算法python代码

以下是DQN算法的Python代码实现：

import random
import numpy as np
import tensorflow as tf
from collections import deque

class DQNAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.memory = deque(maxlen=2000)
        self.gamma = 0.95
        self.epsilon = 1.0
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.learning_rate = 0.001
        self.model = self._build_model()

    def _build_model(self):
        model = tf.keras.models.Sequential()
        model.add(tf.keras.layers.Dense(24, input_dim=self.state_size, activation='relu'))
        model.add(tf.keras.layers.Dense(24, activation='relu'))
        model.add(tf.keras.layers.Dense(self.action_size, activation='linear'))
        model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(lr=self.learning_rate))
        return model

    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))

    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return random.randrange(self.action_size)
        act_values = self.model.predict(state)
        return np.argmax(act_values[0])

    def replay(self, batch_size):
        minibatch = random.sample(self.memory, batch_size)
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                target = (reward + self.gamma * np.amax(self.model.predict(next_state)[0]))
            target_f = self.model.predict(state)
            target_f[0][action] = target
            self.model.fit(state, target_f, epochs=1, verbose=0)
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

    def load(self, name):
        self.model.load_weights(name)

    def save(self, name):
        self.model.save_weights(name)

这个代码实现了一个DQNAgent类，包括以下方法：

- `__init__(self, state_size, action_size)`：初始化DQNAgent的实例，其中state_size表示状态空间的维度，action_size表示动作空间的维度。
- `_build_model(self)`：构建DQN模型，包括输入层、2个隐藏层和输出层。
- `remember(self, state, action, reward, next_state, done)`：记忆当前的状态、动作、奖励、下一个状态和是否终止。
- `act(self, state)`：选择当前状态下的动作，根据当前状态和epsilon参数来选择epsilon-greedy策略或者随机选择动作。
- `replay(self, batch_size)`：从记忆库中取出batch_size个样本进行训练，使用当前模型的预测值与target值计算MSE损失并进行反向传播更新模型参数。
- `load(self, name)`：从文件中加载模型参数。
- `save(self, name)`：将模型参数保存到文件中。

使用DQNAgent类进行训练的示例代码：

import gym

env = gym.make('CartPole-v1')
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
agent = DQNAgent(state_size, action_size)

EPISODES = 1000
for e in range(EPISODES):
    state = env.reset()
    state = np.reshape(state, [1, state_size])
    done = False
    score = 0
    while not done:
        action = agent.act(state)
        next_state, reward, done, _ = env.step(action)
        next_state = np.reshape(next_state, [1, state_size])
        agent.remember(state, action, reward, next_state, done)
        state = next_state
        score += reward
        if done:
            print("episode: {}/{}, score: {}".format(e, EPISODES, score))
            break
    if len(agent.memory) > batch_size:
        agent.replay(batch_size)

agent.save("cartpole-dqn.h5")

在这个示例中，我们使用OpenAI Gym中的CartPole环境进行训练，DQNAgent的状态空间维度为4，动作空间维度为2。我们训练1000个episode，并在episode结束时输出当前episode的得分。每个episode结束后，我们从记忆库中随机选择batch_size个样本进行训练。最后，我们将训练好的模型参数保存到cartpole-dqn.h5文件中。