Bekerja dengan distribusi diskret

Anda akan segera bekerja dengan kebijakan stokastik: kebijakan yang merepresentasikan perilaku agen pada suatu keadaan sebagai distribusi probabilitas atas aksi.

PyTorch dapat merepresentasikan distribusi diskret menggunakan kelas torch.distributions.Categorical, yang akan Anda coba sekarang.

Anda akan melihat bahwa sebenarnya tidak perlu angka-angka yang digunakan sebagai masukan berjumlah 1 seperti probabilitas; angka-angka tersebut akan dinormalisasi secara otomatis.

Latihan ini adalah bagian dari kursus

Deep Reinforcement Learning dengan Python

Petunjuk latihan

Instansiasikan distribusi probabilitas kategorikal.
Ambil satu sampel dari distribusi tersebut.
Tentukan 3 angka positif yang berjumlah 1, untuk bertindak sebagai probabilitas.
Tentukan 5 angka positif; Categorical akan menormalkannya secara diam-diam untuk memperoleh probabilitas.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

from torch.distributions import Categorical

def sample_from_distribution(probs):
    print(f"\nInput: {probs}")
    probs = torch.tensor(probs, dtype=torch.float32)
    # Instantiate the categorical distribution
    dist = ____(probs)
    # Take one sample from the distribution
    sampled_index = ____
    print(f"Taking one sample: index {sampled_index}, with associated probability {dist.probs[sampled_index]:.2f}")

# Specify 3 positive numbers summing to 1
sample_from_distribution([.3, ____, ____])
# Specify 5 positive numbers that do not sum to 1
sample_from_distribution([2, ____, ____, ____, ____])

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: Pengantar policy gradient Exercise 2: Arsitektur policy network Exercise 3: Bekerja dengan distribusi diskret

Latihan Saat Ini

Exercise 4: Policy gradient dan REINFORCE Exercise 5: Pemilihan aksi dalam REINFORCE Exercise 6: Melatih algoritma REINFORCE Exercise 7: Advantage Actor-Critic Exercise 8: Jaringan critic Exercise 9: Perhitungan rugi Actor-Critic Exercise 10: Melatih algoritma A2C

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!