La funzione obiettivo surrogata con clipping

Implementa la funzione calculate_loss() per PPO. Questo richiede di codificare l'innovazione chiave di PPO: la funzione di loss surrogata con clipping. Serve a limitare l'aggiornamento della policy per evitare che si allontani troppo dalla policy precedente a ogni passo.

La formula per l'obiettivo surrogato con clipping è

Nel tuo environment, l'hyperparameter di clipping epsilon è impostato a 0.2.

Questo esercizio fa parte del corso

Deep Reinforcement Learning in Python

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Istruzioni dell'esercizio

Ottieni i rapporti di probabilità tra \pi_\theta e \pi_{\theta_{old}} (versioni non troncata e troncata).
Calcola gli obiettivi surrogati (versioni non troncata e troncata).
Calcola l'obiettivo surrogato con clipping di PPO.
Calcola la loss dell'actor.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

def calculate_losses(critic_network, action_log_prob, action_log_prob_old,
                     reward, state, next_state, done):
    value = critic_network(state)
    next_value = critic_network(next_state)
    td_target = (reward + gamma * next_value * (1-done))
    td_error = td_target - value
    # Obtain the probability ratios
    ____, ____ = calculate_ratios(action_log_prob, action_log_prob_old, epsilon=.2)
    # Calculate the surrogate objectives
    surr1 = ratio * ____.____()
    surr2 = clipped_ratio * ____.____()    
    # Calculate the clipped surrogate objective
    objective = torch.min(____, ____)
    # Calculate the actor loss
    actor_loss = ____
    critic_loss = td_error ** 2
    return actor_loss, critic_loss
  
actor_loss, critic_loss = calculate_losses(critic_network, action_log_prob, action_log_prob_old,
                                           reward, state, next_state, done)
print(actor_loss, critic_loss)

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Questo esercizio fa parte del corso

Deep Reinforcement Learning in Python

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4.8+

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Discover how deep reinforcement learning improves upon traditional Reinforcement Learning while studying and implementing your first Deep Q Learning algorithm.

Exercise 1: Introduction to deep reinforcement learning Exercise 2: Environment and neural network setup Exercise 3: DRL training loop Exercise 4: Introduction to deep Q learning Exercise 5: Deep learning and DQN Exercise 6: The Q-Network architecture Exercise 7: Instantiating the Q-Network Exercise 8: The barebone DQN algorithm Exercise 9: Barebone DQN action selection Exercise 10: Barebone DQN loss function Exercise 11: Training the barebone DQN

Dive into Deep Q-learning by implementing the original DQN algorithm, featuring Experience Replay, epsilon-greediness and fixed Q-targets. Beyond DQN, you will then explore two fascinating extensions that improve the performance and stability of Deep Q-learning: Double DQN and Prioritized Experience Replay.

Exercise 1: DQN with experience replay Exercise 2: The double-ended queue Exercise 3: Experience replay buffer Exercise 4: DQN with experience replay Exercise 5: The complete DQN algorithm Exercise 6: Epsilon-greediness Exercise 7: Fixed Q-targets Exercise 8: Implementing the complete DQN algorithm Exercise 9: Double DQN Exercise 10: Online network and target network in DDQN Exercise 11: Training the double DQN Exercise 12: Prioritized experience replay Exercise 13: Prioritized experience replay buffer Exercise 14: Sampling from the PER buffer Exercise 15: DQN with prioritized experience replay

Learn about the foundational concepts of policy gradient methods found in DRL. You will begin with the policy gradient theorem, which forms the basis for these methods. Then, you will implement the REINFORCE algorithm, a powerful approach to learning policies. The chapter will then guide you through Actor-Critic methods, focusing on the Advantage Actor-Critic (A2C) algorithm, which combines the strengths of both policy gradient and value-based methods to enhance learning efficiency and stability.

Exercise 1: Introduction to policy gradient Exercise 2: The policy network architecture Exercise 3: Working with discrete distributions Exercise 4: Policy gradient and REINFORCE Exercise 5: Action selection in REINFORCE Exercise 6: Training the REINFORCE algorithm Exercise 7: Advantage Actor Critic Exercise 8: Critic network Exercise 9: Actor Critic loss calculations Exercise 10: Training the A2C algorithm

Explore Proximal Policy Optimization (PPO) for robust DRL performance. Next, you will examine using an entropy bonus in PPO, which encourages exploration by preventing premature convergence to deterministic policies. You'll also learn about batch updates in policy gradient methods. Finally, you will learn about hyperparameter optimization with Optuna, a powerful tool for optimizing performance in your DRL models.

Exercise 1: Proximal Policy Optimization Exercise 2: Il rapporto di probabilità con clipping Exercise 3: La funzione obiettivo surrogata con clipping

Esercizio in corso

Exercise 4: Entropia aggiuntiva ed entro PPO Exercise 5: Gioca con l'entropia Exercise 6: Addestrare l'algoritmo PPO Exercise 7: Aggiornamenti in batch nel policy gradient Exercise 8: Minibatch e DRL Exercise 9: A2C con aggiornamenti in batch Exercise 10: Ottimizzazione degli iperparametri con Optuna Exercise 11: Iperparametro o no?Exercise 12: Mettiamo le mani su Optuna Exercise 13: Congratulazioni!