dqn算法python代码解决np问题

D (Deep Q-Network) 是一种使用深度神经网络来实现智能体控制的强化学习算法。下面是一个简单的 DQN 的 Python 代码示例，用来解决一个 np 问题（可能是指 NumPy 库）：

import random
import gym
import numpy as np
from collections import deque
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam

# 定义 DQN Agent 类
class DQNAgent:
    def __init__(self, state_size, action_size):
        # 初始化参数
        self.state_size = state_size
        self.action_size = action_size
        self.memory = deque(maxlen=2000)
        self.gamma = 0.95  # 折扣因子
        self.epsilon = 1.0  # 探索率
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.learning_rate = 0.001
        self.model = self._build_model()

    def _build_model(self):
        # 构建神经网络模型
        model = Sequential()
        model.add(Dense(24, input_dim=self.state_size, activation='relu'))
        model.add(Dense(24, activation='relu'))
        model.add(Dense(self.action_size, activation='linear'))
        model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
        return model

    def act(self, state):
        # 根据当前状态选择动作
        if np.random.rand() <= self.epsilon:
            return random.randrange(self.action_size)
        act_values = self.model.predict(state)
        return np.argmax(act_values = target
            self.model.fit(state, target_f, epochs=1, verbose=0)
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

# 定义环境和智能体
env = gym.make('CartPole-v1')
state_size = env.observation_space.shape<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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-  *3* [python实现DQN代码](https://blog.csdn.net/bajiaoyu517/article/details/129063353)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
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