7个最流行的强化学习算法实战案例（附 Python 代码)

大家好，目前流行的强化学习算法包括 Q-learning、SARSA、DDPG、A2C、PPO、DQN 和 TRPO。

这些算法已被用于在游戏、机器人和决策制定等各种应用中，并且这些流行的算法还在不断发展和改进，本文我们将对其做一个简单的介绍。

技术交流

技术要学会分享、交流，不建议闭门造车。 本文技术由粉丝群小伙伴推荐分享。源码、数据、技术交流提升，均可加交流群获取，群友已超过3000人，添加时最好的备注方式为：来源+兴趣方向，方便找到志同道合的朋友。

方式①、添加微信号：dkl88191，备注：来自CSDN +技术交流
方式②、微信搜索公众号：Python学习与数据挖掘，后台回复：加群

1、Q-learning

Q-learning：Q-learning 是一种无模型、非策略的强化学习算法。它使用 Bellman 方程估计最佳动作值函数，该方程迭代地更新给定状态动作对的估计值。Q-learning 以其简单性和处理大型连续状态空间的能力而闻名。

下面是一个使用 Python 实现 Q-learning 的简单示例：

import numpy as np  
  
# Define the Q-table and the learning rate  
Q = np.zeros((state_space_size, action_space_size))  
alpha = 0.1  
  
# Define the exploration rate and discount factor  
epsilon = 0.1  
gamma = 0.99  
  
for episode in range(num_episodes):  
    current_state = initial_state  
    while not done:  
        # Choose an action using an epsilon-greedy policy  
        if np.random.uniform(0, 1) < epsilon:  
            action = np.random.randint(0, action_space_size)  
        else:  
            action = np.argmax(Q[current_state])  
  
        # Take the action and observe the next state and reward  
        next_state, reward, done = take_action(current_state, action)  
  
        # Update the Q-table using the Bellman equation  
        Q[current_state, action] = Q[current_state, action] + alpha * (  
                reward + gamma * np.max(Q[next_state]) - Q[current_state, action])  
  
        current_state = next_state  

上面的示例中，state_space_size 和 action_space_size 分别是环境中的状态数和动作数。num_episodes 是要为运行算法的轮次数。initial_state 是环境的起始状态。take_action(current_state, action) 是一个函数，它将当前状态和一个动作作为输入，并返回下一个状态、奖励和一个指示轮次是否完成的布尔值。

在 while 循环中，使用 epsilon-greedy 策略根据当前状态选择一个动作。使用概率 epsilon选择一个随机动作，使用概率 1-epsilon选择对当前状态具有最高 Q 值的动作。

采取行动后，观察下一个状态和奖励，使用Bellman方程更新q。并将当前状态更新为下一个状态。这只是 Q-learning 的一个简单示例，并未考虑 Q-table 的初始化和要解决的问题的具体细节。

2、SARSA

SARSA：SARSA 是一种无模型、基于策略的强化学习算法。它也使用Bellman方程来估计动作价值函数，但它是基于下一个动作的期望值，而不是像 Q-learning 中的最优动作。SARSA 以其处理随机动力学问题的能力而闻名。

import numpy as np  
  
# Define the Q-table and the learning rate  
Q = np.zeros((state_space_size, action_space_size))  
alpha = 0.1  
  
# Define the exploration rate and discount factor  
epsilon = 0.1  
gamma = 0.99  
  
for episode in range(num_episodes):  
    current_state = initial_state  
    action = epsilon_greedy_policy(epsilon, Q, current_state)  
    while not done:  
        # Take the action and observe the next state and reward  
        next_state, reward, done = take_action(current_state, action)  
        # Choose next action using epsilon-greedy policy  
        next_action = epsilon_greedy_policy(epsilon, Q, next_state)  
        # Update the Q-table using the Bellman equation  
        Q[current_state, action] = Q[current_state, action] + alpha * (  
                reward + gamma * Q[next_state, next_action] - Q[current_state, action])  
        current_state = next_state  
        action = next_action

state_space_size和action_space_size分别是环境中的状态和操作的数量。num_episodes是您想要运行SARSA算法的轮次数。Initial_state是环境的初始状态。take_action(current_state, action)是一个将当前状态和作为操作输入的函数，并返回下一个状态、奖励和一个指示情节是否完成的布尔值。

在while循环中，使用在单独的函数epsilon_greedy_policy(epsilon, Q, current_state)中定义的epsilon-greedy策略来根据当前状态选择操作。使用概率 epsilon选择一个随机动作，使用概率 1-epsilon对当前状态具有最高 Q 值的动作。

上面与Q-learning相同，但是采取了一个行动后，在观察下一个状态和奖励时它然后使用贪心策略选择下一个行动。并使用Bellman方程更新q表。

3、DDPG

DDPG 是一种用于连续动作空间的无模型、非策略算法。它是一种actor-critic算法，其中actor网络用于选择动作，而critic网络用于评估动作。DDPG 对于机器人控制和其他连续控制任务特别有用。

import numpy as np  
from keras.models import Model, Sequential  
from keras.layers import Dense, Input  
from keras.optimizers import Adam  
  
# Define the actor and critic models  
actor = Sequential()  
actor.add(Dense(32, input_dim=state_space_size, activation='relu'))  
actor.add(Dense(32, activation='relu'))  
actor.add(Dense(action_space_size, activation='tanh'))  
actor.compile(loss='mse', optimizer=Adam(lr=0.001))  
  
critic = Sequential()  
critic.add(Dense(32, input_dim=state_space_size, activation='relu'))  
critic.add(Dense(32, activation='relu'))  
critic.add(Dense(1, activation='linear'))  
critic.compile(loss='mse', optimizer=Adam(lr=0.001))  
  
# Define the replay buffer  
replay_buffer = []  
  
# Define the exploration noise  
exploration_noise = OrnsteinUhlenbeckProcess(size=action_space_size, theta=0.15, mu=0, sigma=0.2)  
  
for episode in range(num_episodes):  
    current_state = initial_state  
    while not done:  
        # Select an action using the actor model and add exploration noise  
        action = actor.predict(current_state)[0] + exploration_noise.sample()  
        action = np.clip(action, -1, 1)  
  
        # Take the action and observe the next state and reward  
        next_state, reward, done = take_action(current_state, action)  
  
        # Add the experience to the replay buffer  
        replay_buffer.append((current_state, action, reward, next_state, done))  
  
        # Sample a batch of experiences from the replay buffer  
        batch = sample(replay_buffer, batch_size)  
  
        # Update the critic model  
        states = np.array([x[0] for x in batch])  
        actions = np.array([x[1] for x in batch])  
        rewards = np.array([x[2] for x in batch])  
        next_states = np.array([x[3] for x in batch])  
  
        target_q_values = rewards + gamma * critic.predict(next_states)  
        critic.train_on_batch(states, target_q_values)  
  
        # Update the actor model  
        action_gradients = np.array(critic.get_gradients(states, actions))  
        actor.train_on_batch(states, action_gradients)  
  
        current_state = next_state  

在本例中，state_space_size和action_space_size分别是环境中的状态和操作的数量。num_episodes是轮次数。Initial_state是环境的初始状态。Take_action (current_state, action)是一个函数，它接受当前状态和操作作为输入，并返回下一个操作。

4、A2C

A2C（Advantage Actor-Critic）是一种有策略的actor-critic算法，它使用Advantage函数来更新策略。该算法实现简单，可以处理离散和连续的动作空间。

import numpy as np  
from keras.models import Model, Sequential  
from keras.layers import Dense, Input  
from keras.optimizers import Adam  
from keras.utils import to_categorical  
  
# Define the actor and critic models  
state_input = Input(shape=(state_space_size,))  
actor = Dense(32, activation='relu')(state_input)  
actor = Dense(32, activation='relu')(actor)  
actor = Dense(action_space_size, activation='softmax')(actor)  
actor_model = Model(inputs=state_input, outputs=actor)  
actor_model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.001))  
  
state_input = Input(shape=(state_space_size,))  
critic = Dense(32, activation='relu')(state_input)  
critic = Dense(32, activation='relu')(critic)  
critic = Dense(1, activation='linear')(critic)  
critic_model = Model(inputs=state_input, outputs=critic)  
critic_model.compile(loss='mse', optimizer=Adam(lr=0.001))  
  
for episode in range(num_episodes):  
    current_state = initial_state  
    done = False  
    while not done:  
        # Select an action using the actor model and add exploration noise  
        action_probs = actor_model.predict(np.array([current_state]))[0]  
        action = np.random.choice(range(action_space_size), p=action_probs)  
  
        # Take the action and observe the next state and reward  
        next_state, reward, done = take_action(current_state, action)  
  
        # Calculate the advantage  
        target_value = critic_model.predict(np.array([next_state]))[0][0]  
        advantage = reward + gamma * target_value - critic_model.predict(np.array([current_state]))[0][0]  
  
        # Update the actor model  
        action_one_hot = to_categorical(action, action_space_size)  
        actor_model.train_on_batch(np.array([current_state]), advantage * action_one_hot)  
  
        # Update the critic model  
        critic_model.train_on_batch(np.array([current_state]), reward + gamma * target_value)  
  
        current_state = next_state  

在这个例子中，actor模型是一个神经网络，它有2个隐藏层，每个隐藏层有32个神经元，具有relu激活函数，输出层具有softmax激活函数。critic模型也是一个神经网络，它有2个隐含层，每层32个神经元，具有relu激活函数，输出层具有线性激活函数。

使用分类交叉熵损失函数训练actor模型，使用均方误差损失函数训练critic模型。动作是根据actor模型预测选择的，并添加了用于探索的噪声。

5、PPO

PPO（Proximal Policy Optimization）是一种策略算法，它使用信任域优化的方法来更新策略。它在具有高维观察和连续动作空间的环境中特别有用。PPO 以其稳定性和高样品效率而著称。

import numpy as np  
from keras.models import Model, Sequential  
from keras.layers import Dense, Input  
from keras.optimizers import Adam  
  
# Define the policy model  
state_input = Input(shape=(state_space_size,))  
policy = Dense(32, activation='relu')(state_input)  
policy = Dense(32, activation='relu')(policy)  
policy = Dense(action_space_size, activation='softmax')(policy)  
policy_model = Model(inputs=state_input, outputs=policy)  
  
# Define the value model  
value_model = Model(inputs=state_input, outputs=Dense(1, activation='linear')(policy))  
  
# Define the optimizer  
optimizer = Adam(lr=0.001)  
  
for episode in range(num_episodes):  
    current_state = initial_state  
    while not done:  
        # Select an action using the policy model  
        action_probs = policy_model.predict(np.array([current_state]))[0]  
        action = np.random.choice(range(action_space_size), p=action_probs)  
  
        # Take the action and observe the next state and reward  
        next_state, reward, done = take_action(current_state, action)  
  
        # Calculate the advantage  
        target_value = value_model.predict(np.array([next_state]))[0][0]  
        advantage = reward + gamma * target_value - value_model.predict(np.array([current_state]))[0][0]  
  
        # Calculate the old and new policy probabilities  
        old_policy_prob = action_probs[action]  
        new_policy_prob = policy_model.predict(np.array([next_state]))[0][action]  
  
        # Calculate the ratio and the surrogate loss  
        ratio = new_policy_prob / old_policy_prob  
        surrogate_loss = np.minimum(ratio * advantage, np.clip(ratio, 1 - epsilon, 1 + epsilon) * advantage)  
  
        # Update the policy and value models  
        policy_model.trainable_weights = value_model.trainable_weights  
        policy_model.compile(optimizer=optimizer, loss=-surrogate_loss)  
        policy_model.train_on_batch(np.array([current_state]), np.array([action_one_hot]))  
        value_model.train_on_batch(np.array([current_state]), reward + gamma * target_value)  
  
        current_state = next_state

6、DQN

DQN（深度 Q 网络）是一种无模型、非策略算法，它使用神经网络来逼近 Q 函数。DQN 特别适用于 Atari 游戏和其他类似问题，其中状态空间是高维的，并使用神经网络近似 Q 函数。

import numpy as np  
from keras.models import Sequential  
from keras.layers import Dense, Input  
from keras.optimizers import Adam  
from collections import deque  
  
# Define the Q-network model  
model = Sequential()  
model.add(Dense(32, input_dim=state_space_size, activation='relu'))  
model.add(Dense(32, activation='relu'))  
model.add(Dense(action_space_size, activation='linear'))  
model.compile(loss='mse', optimizer=Adam(lr=0.001))  
  
# Define the replay buffer  
replay_buffer = deque(maxlen=replay_buffer_size)  
  
for episode in range(num_episodes):  
    current_state = initial_state  
    while not done:  
        # Select an action using an epsilon-greedy policy  
        if np.random.rand() < epsilon:  
            action = np.random.randint(0, action_space_size)  
        else:  
            action = np.argmax(model.predict(np.array([current_state]))[0])  
  
        # Take the action and observe the next state and reward  
        next_state, reward, done = take_action(current_state, action)  
  
        # Add the experience to the replay buffer  
        replay_buffer.append((current_state, action, reward, next_state, done))  
  
        # Sample a batch of experiences from the replay buffer  
        batch = random.sample(replay_buffer, batch_size)  
  
        # Prepare the inputs and targets for the Q-network  
        inputs = np.array([x[0] for x in batch])  
        targets = model.predict(inputs)  
        for i, (state, action, reward, next_state, done) in enumerate(batch):  
            if done:  
                targets[i, action] = reward  
            else:  
                targets[i, action] = reward + gamma * np.max(model.predict(np.array([next_state]))[0])  
  
        # Update the Q-network  
        model.train_on_batch(inputs, targets)  
  
        current_state = next_state  

上面的代码，Q-network有2个隐藏层，每个隐藏层有32个神经元，使用relu激活函数。该网络使用均方误差损失函数和Adam优化器进行训练。

7、TRPO

TRPO （Trust Region Policy Optimization）是一种无模型的策略算法，它使用信任域优化方法来更新策略。它在具有高维观察和连续动作空间的环境中特别有用。

TRPO 是一个复杂的算法，需要多个步骤和组件来实现。TRPO不是用几行代码就能实现的简单算法。

所以我们这里使用实现了TRPO的现有库，例如OpenAI Baselines，它提供了包括TRPO在内的各种预先实现的强化学习算法，。

要在OpenAI Baselines中使用TRPO，我们需要安装:

pip install baselines  

然后可以使用baselines库中的trpo_mpi模块在你的环境中训练TRPO代理，这里有一个简单的例子:

import gym  
from baselines.common.vec_env.dummy_vec_env import DummyVecEnv  
from baselines.trpo_mpi import trpo_mpi  
  
# Initialize the environment  
env = gym.make("CartPole-v1")  
env = DummyVecEnv([lambda: env])  
  
# Define the policy network  
policy_fn = mlp_policy  
  
# Train the TRPO model  
model = trpo_mpi.learn(env, policy_fn, max_iters=1000)

我们使用Gym库初始化环境。然后定义策略网络，并调用TRPO模块中的learn()函数来训练模型。

还有许多其他库也提供了TRPO的实现，例如TensorFlow、PyTorch和RLLib。下面时一个使用TF 2.0实现的样例

import tensorflow as tf  
import gym  
  
  
# Define the policy network  
class PolicyNetwork(tf.keras.Model):  
    def __init__(self):  
        super(PolicyNetwork, self).__init__()  
        self.dense1 = tf.keras.layers.Dense(16, activation='relu')  
        self.dense2 = tf.keras.layers.Dense(16, activation='relu')  
        self.dense3 = tf.keras.layers.Dense(1, activation='sigmoid')  
  
    def call(self, inputs):  
        x = self.dense1(inputs)  
        x = self.dense2(x)  
        x = self.dense3(x)  
        return x  
  
  
# Initialize the environment  
env = gym.make("CartPole-v1")  
  
# Initialize the policy network  
policy_network = PolicyNetwork()  
  
# Define the optimizer  
optimizer = tf.optimizers.Adam()  
  
# Define the loss function  
loss_fn = tf.losses.BinaryCrossentropy()  
  
# Set the maximum number of iterations  
max_iters = 1000  
  
# Start the training loop  
for i in range(max_iters):  
    # Sample an action from the policy network  
    action = tf.squeeze(tf.random.categorical(policy_network(observation), 1))  
  
    # Take a step in the environment  
    observation, reward, done, _ = env.step(action)  
  
    with tf.GradientTape() as tape:  
        # Compute the loss  
        loss = loss_fn(reward, policy_network(observation))  
  
    # Compute the gradients  
    grads = tape.gradient(loss, policy_network.trainable_variables)  
  
    # Perform the update step  
    optimizer.apply_gradients(zip(grads, policy_network.trainable_variables))  
  
    if done:  
        # Reset the environment  
        observation = env.reset()  

在这个例子中，我们首先使用TensorFlow的Keras API定义一个策略网络。然后使用Gym库和策略网络初始化环境。然后定义用于训练策略网络的优化器和损失函数。

在训练循环中，从策略网络中采样一个动作，在环境中前进一步，然后使用TensorFlow的GradientTape计算损失和梯度。然后我们使用优化器执行更新步骤。

这是一个简单的例子，只展示了如何在TensorFlow 2.0中实现TRPO。TRPO是一个非常复杂的算法，这个例子没有涵盖所有的细节，但它是试验TRPO的一个很好的起点。

总结

以上就是我们总结的7个常用的强化学习算法，这些算法并不相互排斥，通常与其他技术(如值函数逼近、基于模型的方法和集成方法)结合使用，可以获得更好的结果。文章来源地址https://www.toymoban.com/news/detail-604960.html

到了这里，关于7个最流行的强化学习算法实战案例（附 Python 代码)的文章就介绍完了。如果您还想了解更多内容，请在右上角搜索TOY模板网以前的文章或继续浏览下面的相关文章，希望大家以后多多支持TOY模板网！