目前流行的強化學(xué)習(xí)算法包括 Q-learning、SARSA、DDPG、A2C、PPO、DQN 和 TRPO。 這些算法已被用于在游戲、機器人和決策制定等各種應(yīng)用中，并且這些流行的算法還在不斷發(fā)展和改進，本文我們將對其做一個簡單的介紹。

【資料圖】

1、Q-learning

Q-learning：Q-learning 是一種無模型、非策略的強化學(xué)習(xí)算法。 它使用 Bellman 方程估計最佳動作值函數(shù)，該方程迭代地更新給定狀態(tài)動作對的估計值。 Q-learning 以其簡單性和處理大型連續(xù)狀態(tài)空間的能力而聞名。

下面是一個使用 Python 實現(xiàn) Q-learning 的簡單示例：

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 分別是環(huán)境中的狀態(tài)數(shù)和動作數(shù)。 num_episodes 是要為運行算法的輪次數(shù)。 initial_state 是環(huán)境的起始狀態(tài)。 take_action(current_state, action) 是一個函數(shù)，它將當(dāng)前狀態(tài)和一個動作作為輸入，并返回下一個狀態(tài)、獎勵和一個指示輪次是否完成的布爾值。

在 while 循環(huán)中，使用 epsilon-greedy 策略根據(jù)當(dāng)前狀態(tài)選擇一個動作。 使用概率 epsilon選擇一個隨機動作，使用概率 1-epsilon選擇對當(dāng)前狀態(tài)具有最高 Q 值的動作。

采取行動后，觀察下一個狀態(tài)和獎勵，使用Bellman方程更新q。 并將當(dāng)前狀態(tài)更新為下一個狀態(tài)。這只是 Q-learning 的一個簡單示例，并未考慮 Q-table 的初始化和要解決的問題的具體細節(jié)。

2、SARSA

SARSA：SARSA 是一種無模型、基于策略的強化學(xué)習(xí)算法。 它也使用Bellman方程來估計動作價值函數(shù)，但它是基于下一個動作的期望值，而不是像 Q-learning 中的最優(yōu)動作。 SARSA 以其處理隨機動力學(xué)問題的能力而聞名。

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分別是環(huán)境中的狀態(tài)和操作的數(shù)量。num_episodes是您想要運行SARSA算法的輪次數(shù)。Initial_state是環(huán)境的初始狀態(tài)。take_action(current_state, action)是一個將當(dāng)前狀態(tài)和作為操作輸入的函數(shù)，并返回下一個狀態(tài)、獎勵和一個指示情節(jié)是否完成的布爾值。

在while循環(huán)中，使用在單獨的函數(shù)epsilon_greedy_policy(epsilon, Q, current_state)中定義的epsilon-greedy策略來根據(jù)當(dāng)前狀態(tài)選擇操作。使用概率 epsilon選擇一個隨機動作，使用概率 1-epsilon對當(dāng)前狀態(tài)具有最高 Q 值的動作。

上面與Q-learning相同，但是采取了一個行動后，在觀察下一個狀態(tài)和獎勵時它然后使用貪心策略選擇下一個行動。并使用Bellman方程更新q表。

3、DDPG

DDPG 是一種用于連續(xù)動作空間的無模型、非策略算法。 它是一種actor-critic算法，其中actor網(wǎng)絡(luò)用于選擇動作，而critic網(wǎng)絡(luò)用于評估動作。 DDPG 對于機器人控制和其他連續(xù)控制任務(wù)特別有用。

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分別是環(huán)境中的狀態(tài)和操作的數(shù)量。num_episodes是輪次數(shù)。Initial_state是環(huán)境的初始狀態(tài)。Take_action (current_state, action)是一個函數(shù)，它接受當(dāng)前狀態(tài)和操作作為輸入，并返回下一個操作。

4、A2C

A2C（Advantage Actor-Critic）是一種有策略的actor-critic算法，它使用Advantage函數(shù)來更新策略。 該算法實現(xiàn)簡單，可以處理離散和連續(xù)的動作空間。

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

在這個例子中，actor模型是一個神經(jīng)網(wǎng)絡(luò)，它有2個隱藏層，每個隱藏層有32個神經(jīng)元，具有relu激活函數(shù)，輸出層具有softmax激活函數(shù)。critic模型也是一個神經(jīng)網(wǎng)絡(luò)，它有2個隱含層，每層32個神經(jīng)元，具有relu激活函數(shù)，輸出層具有線性激活函數(shù)。

使用分類交叉熵損失函數(shù)訓(xùn)練actor模型，使用均方誤差損失函數(shù)訓(xùn)練critic模型。動作是根據(jù)actor模型預(yù)測選擇的，并添加了用于探索的噪聲。

5、PPO

PPO（Proximal Policy Optimization）是一種策略算法，它使用信任域優(yōu)化的方法來更新策略。 它在具有高維觀察和連續(xù)動作空間的環(huán)境中特別有用。 PPO 以其穩(wěn)定性和高樣品效率而著稱。

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 網(wǎng)絡(luò)）是一種無模型、非策略算法，它使用神經(jīng)網(wǎng)絡(luò)來逼近 Q 函數(shù)。 DQN 特別適用于 Atari 游戲和其他類似問題，其中狀態(tài)空間是高維的，并使用神經(jīng)網(wǎng)絡(luò)近似 Q 函數(shù)。

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

上面的代碼，Q-network有2個隱藏層，每個隱藏層有32個神經(jīng)元，使用relu激活函數(shù)。該網(wǎng)絡(luò)使用均方誤差損失函數(shù)和Adam優(yōu)化器進行訓(xùn)練。

7、TRPO

TRPO （Trust Region Policy Optimization）是一種無模型的策略算法，它使用信任域優(yōu)化方法來更新策略。 它在具有高維觀察和連續(xù)動作空間的環(huán)境中特別有用。

TRPO 是一個復(fù)雜的算法，需要多個步驟和組件來實現(xiàn)。TRPO不是用幾行代碼就能實現(xiàn)的簡單算法。

所以我們這里使用實現(xiàn)了TRPO的現(xiàn)有庫，例如OpenAI Baselines，它提供了包括TRPO在內(nèi)的各種預(yù)先實現(xiàn)的強化學(xué)習(xí)算法，。

要在OpenAI Baselines中使用TRPO，我們需要安裝:

pip install baselines

然后可以使用baselines庫中的trpo_mpi模塊在你的環(huán)境中訓(xùn)練TRPO代理，這里有一個簡單的例子:

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)

我們使用Gym庫初始化環(huán)境。然后定義策略網(wǎng)絡(luò)，并調(diào)用TRPO模塊中的learn()函數(shù)來訓(xùn)練模型。

還有許多其他庫也提供了TRPO的實現(xiàn)，例如TensorFlow、PyTorch和RLLib。下面時一個使用TF 2.0實現(xiàn)的樣例

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()

在這個例子中，我們首先使用TensorFlow的Keras API定義一個策略網(wǎng)絡(luò)。然后使用Gym庫和策略網(wǎng)絡(luò)初始化環(huán)境。然后定義用于訓(xùn)練策略網(wǎng)絡(luò)的優(yōu)化器和損失函數(shù)。

在訓(xùn)練循環(huán)中，從策略網(wǎng)絡(luò)中采樣一個動作，在環(huán)境中前進一步，然后使用TensorFlow的GradientTape計算損失和梯度。然后我們使用優(yōu)化器執(zhí)行更新步驟。

這是一個簡單的例子，只展示了如何在TensorFlow 2.0中實現(xiàn)TRPO。TRPO是一個非常復(fù)雜的算法，這個例子沒有涵蓋所有的細節(jié)，但它是試驗TRPO的一個很好的起點。

總結(jié)

以上就是我們總結(jié)的7個常用的強化學(xué)習(xí)算法，這些算法并不相互排斥，通常與其他技術(shù)(如值函數(shù)逼近、基于模型的方法和集成方法)結(jié)合使用，可以獲得更好的結(jié)果。

關(guān)鍵詞： 強化學(xué)習(xí) 激活函數(shù) 損失函數(shù) 神經(jīng)網(wǎng)絡(luò) 狀態(tài)空間