7个流行的强化学习算法及代码实现

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。

 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。

 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。

 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

 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

 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

 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

 pip install baselines

 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)

 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()

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。

END

（添加请备注公司名和职称）

手机芯片这个功能，有望改变市场格局！