Eleştirmen (Critic) ağı

Actor-Critic yöntemleri iki farklı sinir ağı gerektirir.

Aktör (actor) ağının mimarisi, REINFORCE'ta kullandığın politika ağınınkiyle aynıdır; bu yüzden PolicyNetwork sınıfını yeniden kullanabilirsin.

Ancak, eleştirmen (critic) ağı şimdiye kadar uygulamadığın bir bileşen. Critic, Q-Ağlarının yaklaştırdığı eylem değer fonksiyonu \(Q(s_t, a_t)\) yerine, durum değer fonksiyonunu \(V(s_t)\) yaklaştırmayı hedefler.

Şimdi A2C'de kullanacağın Critic ağ modülünü uygulayacaksın.

Bu egzersiz

Python ile Deep Reinforcement Learning

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

İkinci tam bağlantılı katmanın istenen boyutunu, tek bir durum değeri üretecek şekilde doldur.
Critic ağı üzerinden ileri geçişin (forward pass) döndürdüğü değeri elde et.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

class Critic(nn.Module):
    def __init__(self, state_size):
        super(Critic, self).__init__()
        self.fc1 = nn.Linear(state_size, 64)
        # Fill in the desired dimensions
        self.fc2 = nn.Linear(____)

    def forward(self, state):
        x = torch.relu(self.fc1(torch.tensor(state)))
        # Calculate the output value
        value = ____
        return value

critic_network = Critic(8)
state_value = critic_network(torch.rand(8))
print('State value:', state_value)

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: Politika gradyanına giriş Exercise 2: Policy ağının mimarisi Exercise 3: Ayrık dağılımlarla çalışmak Exercise 4: Policy gradient ve REINFORCE Exercise 5: REINFORCE'ta eylem seçimi Exercise 6: REINFORCE algoritmasını eğitme Exercise 7: Advantage Actor-Critic Exercise 8: Eleştirmen (Critic) ağı

Geçerli Egzersiz

Exercise 9: Actor-Critic kayıp hesaplamaları Exercise 10: A2C algoritmasını eğitme

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!