dq_training.py

for e in range(0, num_of_episodes):
    # Reset the enviroment
    state = enviroment.reset()
    state = np.reshape(state, [1, 1])
    
    # Initialize variables
    reward = 0
    terminated = False
    
    bar = progressbar.ProgressBar(maxval=timesteps_per_episode/10, widgets=[progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()])
    bar.start()
    
    for timestep in range(timesteps_per_episode):
        # Run Action
        action = agent.act(state)
        
        # Take action    
        next_state, reward, terminated, info = enviroment.step(action) 
        next_state = np.reshape(next_state, [1, 1])
        agent.store(state, action, reward, next_state, terminated)
        
        state = next_state
        
        if terminated:
            agent.alighn_target_model()
            break
            
        if len(agent.expirience_replay) > batch_size:
            agent.retrain(batch_size)
        
        if timestep%10 == 0:
            bar.update(timestep/10 + 1)
    
    bar.finish()
    if (e + 1) % 10 == 0:
        print("**********************************")
        print("Episode: {}".format(e + 1))
        enviroment.render()
        print("**********************************")