DQN met experience replay

Je gaat nu Experience Replay introduceren om een agent te trainen met een Deep Q Network. Je gebruikt dezelfde Lunar Lander-omgeving als bij het bouwen van je Barebone DQN.

In plaats van bij elke stap alleen de leerervaring van de meest recente transitie te gebruiken om het netwerk te updaten, laat de Experience Replay-buffer de agent leren van een willekeurige batch recente ervaringen. Dit verbetert de mogelijkheid om over de omgeving te leren aanzienlijk.

De QNetwork- en ReplayBuffer-klassen uit eerdere oefeningen zijn beschikbaar en als volgt geïnstantieerd:

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

De functie describe_episode() is ook weer beschikbaar om aan het einde van elke episode de statistieken te beschrijven.

Deze oefening maakt deel uit van de cursus

Deep Reinforcement Learning in Python

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Probeer deze oefening eens door deze voorbeeldcode in te vullen.

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)

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Deze oefening maakt deel uit van de cursus

Deep Reinforcement Learning in Python

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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 met experience replay Exercise 2: De double-ended queue Exercise 3: Experience replay-buffer Exercise 4: DQN met experience replay

Huidige oefening

Exercise 5: Het complete DQN-algoritme Exercise 6: Epsilon-greediness Exercise 7: Gefixeerde Q-targets Exercise 8: Het complete DQN-algoritme implementeren Exercise 9: Double DQN Exercise 10: Online netwerk en targetnetwerk in DDQN Exercise 11: De Double DQN trainen Exercise 12: Prioritized experience replay Exercise 13: Prioritized experience replay-buffer Exercise 14: Steekproeven uit de PER-buffer Exercise 15: DQN met prioritaire 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!