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关于深度学习:7个流行的强化学习算法及代码实现

目前风行的强化学习算法包含 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利用中,并且这些风行的算法还在一直倒退和改良,本文咱们将对其做一个简略的介绍。

1、Q-learning

Q-learning:Q-learning 是一种无模型、非策略的强化学习算法。它应用 Bellman 方程预计最佳动作值函数,该方程迭代地更新给定状态动作对的估计值。Q-learning 以其简略性和解决大型间断状态空间的能力而闻名。

上面是一个应用 Python 实现 Q-learning 的简略示例:

 importnumpyasnp
 
 # 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
 
 forepisodeinrange(num_episodes):
     current_state=initial_state
     whilenotdone:
         # Choose an action using an epsilon-greedy policy
         ifnp.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

下面的示例中,state_space_size 和 action_space_size 别离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,它将以后状态和一个动作作为输出,并返回下一个状态、处分和一个批示轮次是否实现的布尔值。

在 while 循环中,应用 epsilon-greedy 策略依据以后状态抉择一个动作。应用概率 epsilon 抉择一个随机动作,应用概率 1-epsilon 抉择对以后状态具备最高 Q 值的动作。

采取行动后,察看下一个状态和处分,应用 Bellman 方程更新 q。并将以后状态更新为下一个状态。这只是 Q-learning 的一个简略示例,并未思考 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。它也应用 Bellman 方程来预计动作价值函数,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 以其解决随机动力学问题的能力而闻名。

 importnumpyasnp
 
 # 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
 
 forepisodeinrange(num_episodes):
     current_state=initial_state
     action=epsilon_greedy_policy(epsilon, Q, current_state)
     whilenotdone:
         # 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 别离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)是一个将以后状态和作为操作输出的函数,并返回下一个状态、处分和一个批示情节是否实现的布尔值。

在 while 循环中,应用在独自的函数 epsilon_greedy_policy(epsilon, Q, current_state)中定义的 epsilon-greedy 策略来依据以后状态抉择操作。应用概率 epsilon 抉择一个随机动作,应用概率 1-epsilon 对以后状态具备最高 Q 值的动作。

下面与 Q -learning 雷同,然而采取了一个口头后,在察看下一个状态和处分时它而后应用贪婪策略抉择下一个口头。并应用 Bellman 方程更新 q 表。

3、DDPG

DDPG 是一种用于间断动作空间的无模型、非策略算法。它是一种 actor-critic 算法,其中 actor 网络用于抉择动作,而 critic 网络用于评估动作。DDPG 对于机器人管制和其余间断管制工作特地有用。

 importnumpyasnp
 fromkeras.modelsimportModel, Sequential
 fromkeras.layersimportDense, Input
 fromkeras.optimizersimportAdam
 
 # 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)
 
 forepisodeinrange(num_episodes):
     current_state=initial_state
     whilenotdone:
         # 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] forxinbatch])
         actions=np.array([x[1] forxinbatch])
         rewards=np.array([x[2] forxinbatch])
         next_states=np.array([x[3] forxinbatch])
 
         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 别离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,它承受以后状态和操作作为输出,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,它应用 Advantage 函数来更新策略。该算法实现简略,能够解决离散和间断的动作空间。

 importnumpyasnp
 fromkeras.modelsimportModel, Sequential
 fromkeras.layersimportDense, Input
 fromkeras.optimizersimportAdam
 fromkeras.utilsimportto_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))
 
 forepisodeinrange(num_episodes):
     current_state=initial_state
     done=False
     whilenotdone:
         # 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 个暗藏层,每个暗藏层有 32 个神经元,具备 relu 激活函数,输入层具备 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,具备 relu 激活函数,输入层具备线性激活函数。

应用分类穿插熵损失函数训练 actor 模型,应用均方误差损失函数训练 critic 模型。动作是依据 actor 模型预测抉择的,并增加了用于摸索的噪声。

5、PPO

PPO(Proximal Policy Optimization)是一种策略算法,它应用信赖域优化的办法来更新策略。它在具备高维察看和间断动作空间的环境中特地有用。PPO 以其稳定性和高样品效率而著称。

 importnumpyasnp
 fromkeras.modelsimportModel, Sequential
 fromkeras.layersimportDense, Input
 fromkeras.optimizersimportAdam
 
 # 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)
 
 forepisodeinrange(num_episodes):
     current_state=initial_state
     whilenotdone:
         # 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,它应用神经网络来迫近 Q 函数。DQN 特地实用于 Atari 游戏和其余相似问题,其中状态空间是高维的,并应用神经网络近似 Q 函数。

 importnumpyasnp
 fromkeras.modelsimportSequential
 fromkeras.layersimportDense, Input
 fromkeras.optimizersimportAdam
 fromcollectionsimportdeque
 
 # 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)
 
 forepisodeinrange(num_episodes):
     current_state=initial_state
     whilenotdone:
         # Select an action using an epsilon-greedy policy
         ifnp.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] forxinbatch])
         targets=model.predict(inputs)
         fori, (state, action, reward, next_state, done) inenumerate(batch):
             ifdone:
                 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

下面的代码,Q-network 有 2 个暗藏层,每个暗藏层有 32 个神经元,应用 relu 激活函数。该网络应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,它应用信赖域优化办法来更新策略。它在具备高维察看和间断动作空间的环境中特地有用。

TRPO 是一个简单的算法,须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略算法。

所以咱们这里应用实现了 TRPO 的现有库,例如 OpenAI Baselines,它提供了包含 TRPO 在内的各种事后实现的强化学习算法,。

要在 OpenAI Baselines 中应用 TRPO,咱们须要装置:

 pip install baselines

而后能够应用 baselines 库中的 trpo_mpi 模块在你的环境中训练 TRPO 代理,这里有一个简略的例子:

 importgym
 frombaselines.common.vec_env.dummy_vec_envimportDummyVecEnv
 frombaselines.trpo_mpiimporttrpo_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)

咱们应用 Gym 库初始化环境。而后定义策略网络,并调用 TRPO 模块中的 learn()函数来训练模型。

还有许多其余库也提供了 TRPO 的实现,例如 TensorFlow、PyTorch 和 RLLib。上面时一个应用 TF 2.0 实现的样例

 importtensorflowastf
 importgym
 
 # Define the policy network
 classPolicyNetwork(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')
 
     defcall(self, inputs):
         x=self.dense1(inputs)
         x=self.dense2(x)
         x=self.dense3(x)
         returnx
 
 # 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
 foriinrange(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)
 
     withtf.GradientTape() astape:
         # 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))
 
     ifdone:
         # Reset the environment
         observation=env.reset()

在这个例子中,咱们首先应用 TensorFlow 的 Keras API 定义一个策略网络。而后应用 Gym 库和策略网络初始化环境。而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,而后应用 TensorFlow 的 GradientTape 计算损失和梯度。而后咱们应用优化器执行更新步骤。

这是一个简略的例子,只展现了如何在 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 的一个很好的终点。

总结

以上就是咱们总结的 7 个罕用的强化学习算法,这些算法并不互相排挤,通常与其余技术 (如值函数迫近、基于模型的办法和集成办法) 联合应用,能够取得更好的后果。

https://avoid.overfit.cn/post/82000e3c65a14403b5e4defae28b703b

作者:Siddhartha Pramanik

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