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

 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

 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()