写一个深度强化学习代码示例

好的，以下是一个简单的深度强化学习代码示例：

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

env = gym.make('CartPole-v1')

# hyperparameters
learning_rate = 0.01
num_iters = 1000
discount = 0.99
epsilon = 1
epsilon_decay = 0.995

# define neural network
inputs = tf.placeholder(shape=[None, env.observation_space.shape[0]], dtype=tf.float32)
actions = tf.placeholder(shape=[None], dtype=tf.int32)
rewards = tf.placeholder(shape=[None], dtype=tf.float32)

fc1 = tf.layers.dense(inputs=inputs, units=24, activation=tf.nn.relu)
fc2 = tf.layers.dense(inputs=fc1, units=24, activation=tf.nn.relu)
output = tf.layers.dense(inputs=fc2, units=env.action_space.n)

action_probs = tf.nn.softmax(output)
chosen_actions = tf.one_hot(actions, env.action_space.n)
action_values = tf.reduce_sum(chosen_actions * output, axis=1)

loss = tf.reduce_mean(tf.square(rewards - action_values))
train_op = tf.train.AdamOptimizer(learning_rate).minimize(loss)

# train agent
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    for i in range(num_iters):
        state = env.reset()
        done = False
        total_reward = 0

        while not done:
            if random.uniform(0, 1) < epsilon:
                action = env.action_space.sample()
            else:
                probs = sess.run(action_probs, feed_dict={inputs: [state]})
                action = np.argmax(probs)

            next_state, reward, done, _ = env.step(action)
            total_reward += reward

            if done:
                target_reward = reward
            else:
                next_probs, next_vals = sess.run([action_probs, output], feed_dict={inputs: [next_state]})                
                target_reward = reward + discount * np.max(next_vals)

            sess.run(train_op, feed_dict={inputs: [state], actions: [action], rewards: [target_reward]})

            state = next_state

        epsilon *= epsilon_decay

        if i % 100 == 0:
            print('iter: {0}, epsilon: {1}, total reward: {2}'.format(i, epsilon, total_reward))

这个示例采用了深度神经网络作为价值函数来学习在游戏中的最佳行动。在每个迭代中，它会重复进行以下步骤直到游戏结束：

1. 根据当前状态计算输出并选择一个行动。
2. 通过环境的 step() 方法来执行行动并获取下一个状态和奖励。
3. 如果达到了终止状态，将目标奖励设置为当前奖励，否则将目标奖励设置为当前奖励加上下一个状态的最高预测值。
4. 使用神经网络进行训练，以便输出接近目标奖励。

在训练过程中，epsilon 值会逐渐递减以使智能体学会更优化的行为策略。