目前流行的強(qiáng)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(jī)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(jìn)，本文我們將對(duì)其做一個(gè)簡(jiǎn)單的介紹。

 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(dòng)作數(shù)。num_episodes 是要為運(yùn)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(gè)函數(shù)，它將當(dāng)前狀態(tài)和一個(gè)動(dòng)作作為輸入，并返回下一個(gè)狀態(tài)、獎(jiǎng)勵(lì)和一個(gè)指示輪次是否完成的布爾值。

 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分別是環(huán)境中的狀態(tài)和操作的數(shù)量。num_episodes是您想要運(yùn)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(gè)將當(dāng)前狀態(tài)和作為操作輸入的函數(shù)，并返回下一個(gè)狀態(tài)、獎(jiǎng)勵(lì)和一個(gè)指示情節(jié)是否完成的布爾值。

 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()

以上就是我們總結(jié)的7個(gè)常用的強(qiáng)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。

END

（添加請(qǐng)備注公司名和職稱）

手機(jī)芯片這個(gè)功能，有望改變市場(chǎng)格局！