最简单的dqn代码 tensorflow

DQN（Deep Q-Network）是一种基于深度学习的强化学习算法，在解决多种游戏和控制问题方面有着广泛的应用。TensorFlow是一种流行的深度学习框架，具有高效和易于使用的特点。在此，我们将介绍一个基于TensorFlow的最简单的DQN代码。

以下是代码的主要部分：

1.导入相关的模块和库

import gym
import tensorflow as tf
import numpy as np
import random

2.设置并初始化相关的超参数

env = gym.make('CartPole-v0')
lr = 0.01
gamma = 0.99
epsilon = 1.0
max_epsilon = 1.0
min_epsilon = 0.01
decay_rate = 0.01
num_episodes = 1000
batch_size = 32
memory_size = 1000000

3.构建并初始化神经网络模型

input_size = env.observation_space.shape[0]
output_size = env.action_space.n
inputs = tf.placeholder(tf.float32, shape=[None, input_size])
W1 = tf.get_variable("W1", shape=[input_size, output_size], initializer=tf.contrib.layers.xavier_initializer())
Qout = tf.matmul(inputs, W1)

predict = tf.argmax(Qout, 1)

4.定义损失函数和优化器

nextQ = tf.placeholder(tf.float32, shape=[None, output_size])
loss = tf.reduce_sum(tf.square(nextQ - Qout))
trainer = tf.train.AdamOptimizer(learning_rate=lr)
updateModel = trainer.minimize(loss)

5.训练模型

sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
memory = []
for episode in range(num_episodes):
    state = env.reset()
    reward_disp = 0
    done = False
    for t in range(1000):
        action, allQ = sess.run([predict, Qout], feed_dict={inputs: np.expand_dims(state, axis=0)})

        if np.random.rand() < epsilon:
            action[0] = env.action_space.sample()

        new_state, reward, done, info = env.step(action[0])

        memory.append((state, action[0], reward, new_state, done))
        if len(memory) > memory_size:
            memory.pop(0)

        if done:
            break

        state = new_state

    if epsilon > min_epsilon:
        epsilon = max_epsilon * np.exp(-decay_rate * episode)

    batch = random.sample(memory, min(len(memory), batch_size))
    states = np.array([i[0] for i in batch])
    actions = np.array([i[1] for i in batch])
    rewards = np.array([i[2] for i in batch])
    new_states = np.array([i[3] for i in batch])
    dones = np.array([i[4] for i in batch])

    Q1 = sess.run(Qout, feed_dict={inputs: new_states})
    Q2 = rewards + gamma * np.max(Q1, axis=1)

    targetQ = sess.run(Qout, feed_dict={inputs: states})
    targetQ[np.arange(batch_size), actions] = Q2

    _, loss_disp = sess.run([updateModel, loss], feed_dict={inputs: states, nextQ: targetQ})
    sum_loss += loss_disp
    sum_reward += reward_disp

    print("Episode {}, Loss: {}, Avg Reward: {}".format(episode + 1, sum_loss / (t + 1), sum_reward / (t + 1)))

以上是一个简单的、基于TensorFlow的DQN代码模板。其中，我们导入了必要的模块和库，并设置和初始化了相关的超参数。我们还构建并初始化了神经网络模型，并定义了损失函数和优化器。在训练模型的过程中，我们首先获取当前状态下的动作和所有可能的Q值，然后根据$\epsilon$-贪心策略随机选择一个动作。之后，我们执行此动作、获取下一个状态和奖励，并将这些信息存储在记忆池中。如果记忆池已满，则删除最早的元素。在每个时刻，我们都更新$\epsilon$的值，并从记忆池中随机采样一批样本进行训练。最后，我们输出损失和平均奖励。