episodes = generate_training_episodes(num_episodes) for episode in episodes: for state, action, reward, next_state in episode: # Get the available actions for the next state next_available_actions = get_available_actions(next_state) # Calculate the maximum Q-value for the next state next_q_values = [Q_table[(next_state, next_action)] for next_action in next_available_actions] max_next_q_value = max(next_q_values) # Update the Q-value for the current state-action pair […]