SARSA training function

Art057_Python_008.py

def train(n_episodes, n_max_steps, start_epsilon, min_epsilon, decay_rate, Qtable):
    for episode in range(n_episodes):
        
        # Reset the environment at the start of each episode
        state, info = env.reset()
        t = 0
        done = False
        
        # Calculate epsilon value based on decay rate
        epsilon = max(min_epsilon, (start_epsilon - min_epsilon)*np.exp(-decay_rate*episode))
        
        # Choose an action using previously defined epsilon-greedy policy
        action = epsilon_greedy(Qtable, state, epsilon)
        
        for t in range(n_max_steps):
            
            # Perform the action in the environment, get reward and next state
            next_state, reward, done, _, info = env.step(action)
            
            # Choose next action
            next_action=epsilon_greedy(Qtable, next_state, epsilon)
            
            # Update Q-table
            Qtable = update_Q(Qtable, state, action, reward, next_state, next_action)
            
            # Update current state 
            state = next_state
            action = next_action
            
            # Finish the episode when done=True, i.e., reached the goal or fallen into a hole
            if done:
                break
                
    # Return final Q-table
    return Qtable