Sampling dari buffer PER

Sebelum Anda dapat menggunakan kelas Prioritized Experience Buffer untuk melatih agen, Anda masih perlu mengimplementasikan metode .sample(). Metode ini menerima argumen berupa ukuran sampel yang ingin Anda ambil, dan mengembalikan transisi yang diambil sebagai tensors, beserta indeksnya dalam buffer memori dan bobot kepentingannya.

Sebuah buffer dengan kapasitas 10 telah dimuat sebelumnya di lingkungan Anda untuk diambil sampelnya.

Latihan ini adalah bagian dari kursus

Deep Reinforcement Learning dengan Python

Petunjuk latihan

Hitung probabilitas pengambilan sampel yang terkait dengan setiap transisi.
Ambil indeks yang sesuai dengan transisi dalam sampel; np.random.choice(a, s, p=p) mengambil sampel berukuran s dengan pengembalian dari array a, berdasarkan array probabilitas p.
Hitung bobot kepentingan yang terkait dengan setiap transisi.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

def sample(self, batch_size):
    priorities = np.array(self.priorities)
    # Calculate the sampling probabilities
    probabilities = ____ / np.sum(____)
    # Draw the indices for the sample
    indices = np.random.choice(____)
    # Calculate the importance weights
    weights = (1 / (len(self.memory) * ____)) ** ____
    weights /= np.max(weights)
    states, actions, rewards, next_states, dones = zip(*[self.memory[idx] for idx in indices])
    weights = [weights[idx] for idx in indices]
    states_tensor = torch.tensor(states, dtype=torch.float32)
    rewards_tensor = torch.tensor(rewards, dtype=torch.float32)
    next_states_tensor = torch.tensor(next_states, dtype=torch.float32)
    dones_tensor = torch.tensor(dones, dtype=torch.float32)
    weights_tensor = torch.tensor(weights, dtype=torch.float32)
    actions_tensor = torch.tensor(actions, dtype=torch.long).unsqueeze(1)
    return (states_tensor, actions_tensor, rewards_tensor, next_states_tensor,
            dones_tensor, indices, weights_tensor)

PrioritizedReplayBuffer.sample = sample
print("Sampled transitions:\n", buffer.sample(3))

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 dengan experience replay Exercise 2: Double-Ended Queue Exercise 3: Buffer experience replay Exercise 4: DQN dengan experience replay Exercise 5: Algoritma DQN yang lengkap Exercise 6: Epsilon-greediness Exercise 7: Fixed Q-targets Exercise 8: Mengimplementasikan algoritme DQN lengkap Exercise 9: Double DQN Exercise 10: Jaringan online dan jaringan target dalam DDQN Exercise 11: Melatih double DQN Exercise 12: Prioritized experience replay Exercise 13: Buffer prioritized experience replay Exercise 14: Sampling dari buffer PER

Latihan Saat Ini

Exercise 15: DQN dengan 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: 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!