DQN dengan experience replay

Sekarang Anda akan menambahkan Experience Replay untuk melatih agen menggunakan Deep Q Network. Anda akan menggunakan lingkungan Lunar Lander yang sama seperti saat membangun Barebone DQN Anda.

Pada setiap langkah, alih-alih hanya menggunakan pembelajaran dari transisi terbaru untuk memperbarui jaringan, buffer Experience Replay memungkinkan agen belajar dari contoh acak pengalaman terbaru. Ini secara signifikan meningkatkan kemampuannya untuk mempelajari lingkungan.

Kelas QNetwork dan ReplayBuffer dari latihan sebelumnya tersedia dan telah diinstansiasi sebagai berikut:

q_network = QNetwork(8, 4)
replay_buffer = ReplayBuffer(10000)

Fungsi describe_episode() juga tersedia kembali untuk mendeskripsikan metrik di akhir setiap episode.

Latihan ini adalah bagian dari kursus

Deep Reinforcement Learning dengan Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

for episode in range(10):
    state, info = env.reset()
    done = False
    step = 0
    episode_reward = 0
    while not done:
        step += 1
        q_values = q_network(state)        
        action = torch.argmax(q_values).item()
        next_state, reward, terminated, truncated, _ = env.step(action)
        done = terminated or truncated
        # Store the latest experience in the replay buffer
        replay_buffer.____        
        state = next_state
        episode_reward += reward    
    describe_episode(episode, reward, episode_reward, step)

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

Latihan Saat Ini

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 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!