Fungsi objektif surrogate terpangkas (clipped)

Implementasikan fungsi calculate_loss() untuk PPO. Ini memerlukan pengodean inovasi kunci PPO — fungsi loss surrogate yang dipangkas (clipped). Pendekatan ini membantu membatasi pembaruan kebijakan agar tidak bergeser terlalu jauh dari kebijakan sebelumnya pada setiap langkah.

Rumus untuk objektif surrogate yang dipangkas adalah

Lingkungan Anda memiliki hiperparameter clipping epsilon yang disetel ke 0.2.

Latihan ini adalah bagian dari kursus

Deep Reinforcement Learning dengan Python

Petunjuk latihan

Peroleh rasio probabilitas antara \pi_\theta dan \pi_{\theta_{old}} (versi belum dipangkas dan dipangkas).
Hitung tujuan surrogate (versi belum dipangkas dan dipangkas).
Hitung objektif surrogate PPO yang dipangkas (clipped).
Hitung loss aktor.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

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)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Deep Reinforcement Learning dengan Python

SkillTag.level.advancedSkillTag.label

4.8+

Mulai Kursus Gratis

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: Optimisasi kebijakan proksimal Exercise 2: Rasio probabilitas yang di-klip Exercise 3: Fungsi objektif surrogate terpangkas (clipped)

Latihan Saat Ini

Exercise 4: Bonus entropi dan PPO Exercise 5: Ruang bermain entropi Exercise 6: Melatih algoritma PPO Exercise 7: Pembaruan batch dalam policy gradient Exercise 8: Minibatch dan DRL Exercise 9: A2C dengan pembaruan batch Exercise 10: Optimisasi hyperparameter dengan Optuna Exercise 11: Hyperparameter atau bukan?Exercise 12: Praktik langsung dengan Optuna Exercise 13: Selamat!