Istanziamento della Q-Network

Ora che ne hai definito l'architettura, sei pronto a istanziare la rete effettiva che l'agente utilizzerà, insieme al suo ottimizzatore. L'ambiente Lunar Lander ha uno spazio degli stati di dimensione 8 e uno spazio delle azioni di dimensione 4 (corrispondenti a 0: non fare nulla, 1: propulsore sinistro, 2: motore principale, 3: propulsore destro).

La classe QNetwork dell'esercizio precedente è a tua disposizione.

Questo esercizio fa parte del corso

Deep Reinforcement Learning in Python

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Istruzioni dell'esercizio

Istanzia una Q Network per l'ambiente Lunar Lander.
Definisci l'ottimizzatore Adam per la rete neurale, specificando un tasso di apprendimento pari a 0.0001.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

state_size = 8
action_size = 4
# Instantiate the Q Network
q_network = QNetwork(____, ____)
# Specify the optimizer learning rate
optimizer = optim.Adam(q_network.parameters(), ____)

print("Q-Network initialized as:\n", q_network)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Deep Reinforcement Learning in Python

AvançadoNível de habilidade

4.8+

Inizia il corso gratis

Discover how deep reinforcement learning improves upon traditional Reinforcement Learning while studying and implementing your first Deep Q Learning algorithm.

Exercise 1: Introduzione al deep reinforcement learning Exercise 2: Configurazione dell'ambiente e della rete neurale Exercise 3: Ciclo di training DRL Exercise 4: Introduzione al deep Q learning Exercise 5: Deep learning e DQN Exercise 6: L'architettura della Q-Network Exercise 7: Istanziamento della Q-Network

Esercizio in corso

Exercise 8: L'algoritmo DQN essenziale Exercise 9: Selezione dell'azione con DQN essenziale Exercise 10: Funzione di loss del DQN essenziale Exercise 11: Addestrare il DQN essenziale

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: 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!