Python OpenAI Gym 中级教程：强化学习实践项目
1. 安装依赖
首先，确保你已经安装了必要的依赖：
代码语言：javascript复制pip install gym[box2d] tensorflow2. 强化学习项目实践
2.1 创建 DQN 模型
我们将使用 TensorFlow 创建一个简单的深度 Q 网络模型。
代码语言：javascript复制import tensorflow as tf
from tensorflow.keras import layers, models
class DQN(models.Model):
def __init__(self, num_actions):
super(DQN, self).__init__()
self.layer1 = layers.Dense(24, activation='relu')
self.layer2 = layers.Dense(24, activation='relu')
self.output_layer = layers.Dense(num_actions, activation='linear')
def call(self, state):
x = self.layer1(state)
x = self.layer2(x)
return self.output_layer(x)2.2 创建经验回放缓冲区
为了训练 DQN 模型，我们将使用经验回放缓冲区来存储过去的经验。
代码语言：javascript复制import random
from collections import deque
class ReplayBuffer:
def __init__(self, capacity):
self.capacity = capacity
self.buffer = deque(maxlen=capacity)
def add(self, experience):
self.buffer.append(experience)
def sample(self, batch_size):
batch = random.sample(self.buffer, batch_size)
states, actions, rewards, next_states, dones = zip(*batch)
return (
tf.concat(states, axis=0),
tf.convert_to_tensor(actions, dtype=tf.float32),
tf.convert_to_tensor(rewards, dtype=tf.float32),
tf.concat(next_states, axis=0),
tf.convert_to_tensor(dones, dtype=tf.float32),
)2.3 DQN 训练
我们将定义一个函数来训练 DQN 模型。
代码语言：javascript复制import numpy as np
import gym
def train_dqn(env, model, target_model, replay_buffer, num_episodes=1000, batch_size=32, gamma=0.99, target_update_frequency=100):
optimizer = tf.optimizers.Adam(learning_rate=0.001)
huber_loss = tf.keras.losses.Huber()
epsilon = 1.0
epsilon_decay = 0.995
min_epsilon = 0.01
for episode in range(1, num_episodes 1):
state = env.reset()
state = tf.convert_to_tensor(state, dtype=tf.float32)
total_reward = 0
while True:
# epsilon-greedy策略选择动作
if np.random.rand() < epsilon:
action = env.action_space.sample()
else:
q_values = model(state[None, :])
action = tf.argmax(q_values[0]).numpy()
next_state, reward, done, _ = env.step(action)
next_state = tf.convert_to_tensor(next_state, dtype=tf.float32)
total_reward = reward
replay_buffer.add((state, action, reward, next_state, done))
state = next_state
# 经验回放
if len(replay_buffer.buffer) >= batch_size:
states, actions, rewards, next_states, dones = replay_buffer.sample(batch_size)
with tf.GradientTape() as tape:
q_values = model(states)
next_q_values = target_model(next_states)
target_q_values = rewards gamma * tf.reduce_max(next_q_values, axis=1) * (1 - dones)
selected_q_values = tf.reduce_sum(q_values * tf.one_hot(actions, env.action_space.n), axis=1)
loss = huber_loss(selected_q_values, target_q_values)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
# 更新目标网络
if episode % target_update_frequency == 0:
target_model.set_weights(model.get_weights())
if done:
epsilon = max(epsilon * epsilon_decay, min_epsilon)
print(f"Episode: {episode}, Total Reward: {total_reward}, Epsilon: {epsilon}")
break2.4 主函数
最后，我们将定义一个主函数来运行我们的强化学习项目。
代码语言：javascript复制if __name__ == "__main__":
# 创建环境和模型
env = gym.make("CartPole-v1")
model = DQN(env.action_space.n)
target_model = DQN(env.action_space.n)
target_model.set_weights(model.get_weights())
# 创建经验回放缓冲区
replay_buffer = ReplayBuffer(capacity=10000)
# 训练 DQN 模型
train_dqn(env, model, target_model, replay_buffer, num_episodes=500)3. 总结
通过这个实际项目，我们演示了如何在 OpenAI Gym 中使用深度 Q 网络（DQN）来解决经典的 CartPole 问题。我们创建了一个简单的 DQN 模型，实现了经验回放缓冲区，并进行了训练。这个项目为初学者提供了一个实践的起点，同时展示了在强化学习任务中使用 TensorFlow 和 OpenAI Gym 的基本步骤。希望这篇博客能够帮助你更好地理解和应用强化学习算法。
