training.py

num_of_episodes = 100000

for episode in range(0, num_of_episodes):
    # Reset the enviroment
    state = enviroment.reset()

    # Initialize variables
    reward = 0
    terminated = False
    
    while not terminated:
        # Take learned path or explore new actions based on the epsilon
        if random.uniform(0, 1) < epsilon:
            action = enviroment.action_space.sample()
        else:
            action = np.argmax(q_table[state])

        # Take action    
        next_state, reward, terminated, info = enviroment.step(action) 
        
        # Recalculate
        q_value = q_table[state, action]
        max_value = np.max(q_table[next_state])
        new_q_value = (1 - alpha) * q_value + alpha * (reward + gamma * max_value)
        
        # Update Q-table
        q_table[state, action] = new_q_value
        state = next_state
        
    if (episode + 1) % 100 == 0:
        clear_output(wait=True)
        print("Episode: {}".format(episode + 1))
        enviroment.render()

print("**********************************")
print("Training is done!\n")
print("**********************************")