Epsilon-açgözlülüğü

Bu egzersizde, azalan epsilon-açgözlülüğü uygulayan bir select_action() fonksiyonu yazacaksın.

Epsilon-açgözlülüğü, aracının ortamı keşfetmesini teşvik eder ve bu da öğrenmeyi iyileştirir!

Epsilon-açgözlülüğü çizelgesi, verilen her step için aşağıdaki formülle bir eşik \(\varepsilon\) belirler: $$\varepsilon = end + (start-end) \cdot e^{-\frac{step}{decay}}$$

select_action() \(\varepsilon\) olasılıkla rastgele bir eylem ve \(1-\varepsilon\) olasılıkla en yüksek Q-değerine sahip eylemi döndürmelidir.

Bu egzersiz

Python ile Deep Reinforcement Learning

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Verilen step değeri için epsilon eşiğini hesapla.
0 ile 1 arasında rastgele bir sayı çek.
epsilon olasılıkla rastgele bir eylem döndür.
1-epsilon olasılıkla en yüksek Q-değerine sahip eylemi döndür.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

def select_action(q_values, step, start, end, decay):
    # Calculate the threshold value for this step
    epsilon = end + (____) * math.exp(____ / ____)
    # Draw a random number between 0 and 1
    sample = random.____
    if sample < epsilon:
        # Return a random action index
        return random.____
    # Return the action index with highest Q-value
    return torch.____.item()
      
for step in [1, 500, 2500]:
    actions = [select_action(torch.Tensor([1, 2, 3, 5]), step, .9, .05, 1000) for _ in range(20)]
    print(f"Selecting 20 actions at step {step}.\nThe action with highest q-value is action 3.\nSelected actions: {actions}\n\n")

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: Deneyim tekrar çalma ile DQN Exercise 2: Çift uçlu kuyruk Exercise 3: Deneyim tekrar oynatma tamponu Exercise 4: Deneyim Tekrarı ile DQN Exercise 5: Tam DQN algoritması Exercise 6: Epsilon-açgözlülüğü

Geçerli Egzersiz

Exercise 7: Sabit Q-hedefleri Exercise 8: Tam DQN algoritmasını uygulama Exercise 9: Double DQN Exercise 10: DDQN'de çevrimiçi ağ ve hedef ağ Exercise 11: Double DQN'i eğitmek Exercise 12: Önceliklendirilmiş deneyim tekrar oynatma Exercise 13: Öncelikli deneyim tekrar oynatma tamponu Exercise 14: PER arabelleğinden örnekleme Exercise 15: Öncelikli deneyim tekrarı ile DQN

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: The clipped probability ratio Exercise 3: The clipped surrogate objective function Exercise 4: Entropy bonus and PPO Exercise 5: Entropy playground Exercise 6: Training the PPO algorithm Exercise 7: Batch updates in policy gradient Exercise 8: Minibatch and DRL Exercise 9: A2C with batch updates Exercise 10: Hyperparameter optimization with Optuna Exercise 11: Hyperparameter or not?Exercise 12: Hands-on with Optuna Exercise 13: Congratulations!