Entropi oyun alanı

Entropi kavramıyla daha önce karşılaşmadıysan, bir örnek üzerinden düşünmek faydalı olur.

Bir olasılık listesini argüman olarak alan plot_probabilities adlı bir fonksiyon yazacaksın. Bu fonksiyon entropiyi hesaplayıp olasılıkları bir çubuk grafikte çizer.

Entropiyle oynadıkça, olasılık dağılımı birçok eyleme yayıldığında entropinin daha yüksek olduğunu göreceksin.

torch.distribution.Categorical sınıfı ortamında Categorical adıyla yüklüdür; bu sınıfın nat cinsinden entropiyi döndüren .entropy() adlı bir yöntemi vardır.

Bu egzersiz

Python ile Deep Reinforcement Learning

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Olasılık dağılımının nat cinsinden entropisini elde et.
Kolaylık olması için entropiyi nattan bite dönüştür.
Fonksiyon için giriş olarak başka bir liste dene.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

def plot_probabilities(probs):
  dist = Categorical(torch.tensor(probs))
  # Obtain the entropy in nats
  entropy = dist.____
  # Convert the entropy to bits
  entropy = entropy / math.log(____)
  print(f"{'Probabilities:':>15} {[round(prob, 3) for prob in dist.probs.tolist()]}")
  print(f"{'Entropy:':>15} {entropy:.2f}\n")
  plt.figure()
  plt.bar([str(x) for x in range(len(dist.probs))], dist.probs, color='skyblue', edgecolor='black')
  plt.ylabel('Probability'); plt.xlabel('Action index'); plt.ylim(0, 1)
  plt.show()
  
plot_probabilities([.25, .25, .25, .25])
plot_probabilities([.1, .15, .2, .25, .3])
# Try with your own list
plot_probabilities(____)

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile Deep Reinforcement Learning

kursunun bir parçasıdır

AvançadoNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: Yakınsal politika optimizasyonu Exercise 2: Kırpılmış olasılık oranı Exercise 3: Kırpılmış yerine geçen amaç fonksiyonu Exercise 4: Entropi bonusu ve PPO Exercise 5: Entropi oyun alanı

Geçerli Egzersiz

Exercise 6: PPO algoritmasını eğitme Exercise 7: Politika gradyanında yığın güncellemeleri Exercise 8: Minibatch ve DRL Exercise 9: Toplu güncellemelerle A2C Exercise 10: Optuna ile hiperparametre optimizasyonu Exercise 11: Hiperparametre mi değil mi?Exercise 12: Optuna ile uygulama Exercise 13: Tebrikler!