```py
import tensorflow as tf
import numpy as np
import gym
import random
import os
from _collections import deque
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# Initial Constants
num_episodes = 500
num_exploration_episodes = 100
max_len_episode = 1000
batch_size = 32
learning_rate = 1e-3
gamma = 1.
initial_epsilon = 1.
final_epsilon = 0.01
# Create Model Class
class QNetwork(tf.keras.Model):
def __init__(self):
super().__init__()
self.dense1 = tf.keras.layers.Dense(24, tf.nn.relu)
self.dense2 = tf.keras.layers.Dense(24, tf.nn.relu)
self.dense3 = tf.keras.layers.Dense(2)
def call(self, inputs):
x = self.dense1(inputs)
x = self.dense2(x)
x = self.dense3(x)
return x
def predict(self, inputs):
q_value = self(inputs)
return tf.argmax(q_value, axis=-1)
if __name__ == '__main__':
env = gym.make('CartPole-v1')
model = QNetwork()
optimizer = tf.keras.optimizers.Adam(learning_rate)
replay_buffer = deque(maxlen=10000)
epsilon = initial_epsilon
for episode_id in range(num_episodes):
state = env.reset()
epsilon = max(initial_epsilon * (num_exploration_episodes - episode_id) / num_exploration_episodes,
final_epsilon)
for t in range(max_len_episode):
env.render()
if random.random() < epsilon:
action = env.action_space.sample() # 探索
else:
action = model.predict(np.expand_dims(state, axis=0)).numpy()
action = action[0]
next_state, reward, done, info = env.step(action)
reward = -10. if done else reward
replay_buffer.append((state, action, reward, next_state, 1 if done else 0))
state = next_state
if done:
print("episode %d, epsilon %f, score %d" % (episode_id, epsilon, t))
break
if len(replay_buffer) >= batch_size:
batch_state, batch_action, batch_reward, batch_next_state, batch_done = zip(
*random.sample(replay_buffer, batch_size)) # 随机抽取数据,然后解压数据
# 修改数据类型
batch_state, batch_reward, batch_next_state, batch_done = [np.array(a, dtype=np.float32)
for a in
[batch_state, batch_reward,
batch_next_state, batch_done]]
batch_action = np.array(batch_action, dtype=np.int32)
# 训练模型
q_value = model(batch_next_state)
y = batch_reward + (gamma * tf.reduce_max(q_value, axis=1)) * (1 - batch_done)
with tf.GradientTape() as tape:
y_pred = tf.reduce_sum(model(batch_state) * tf.one_hot(batch_action, depth=2), axis=1)
loss = tf.keras.losses.mean_squared_error(y_true=y, y_pred=y_pred)
grads = tape.gradient(loss, model.variables)
optimizer.apply_gradients(grads_and_vars=zip(grads, model.variables))
```

深度强化学习 Deep Q-Learning 练习