Epsilon-greediness

In questo esercizio implementerai una funzione select_action() che applica l'epsilon-greediness con decadimento.

L'epsilon-greediness spingerà il tuo agente a esplorare l'ambiente, migliorando l'apprendimento!

Lo schedule di epsilon-greediness determina una soglia \(\varepsilon\) per un dato step, secondo la formula: $$\varepsilon = end + (start-end) \cdot e^{-\frac{step}{decay}}$$

select_action() dovrebbe restituire un'azione casuale con probabilità \(\varepsilon\) e l'azione con Q-value più alto con probabilità \(1-\varepsilon\).

Questo esercizio fa parte del corso

Deep Reinforcement Learning in Python

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Istruzioni dell'esercizio

Calcola la soglia epsilon per il valore dato di step.
Estrai un numero casuale tra 0 e 1.
Con probabilità epsilon, restituisci un'azione casuale.
Con probabilità 1-epsilon, restituisci l'azione con Q-value più alto.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

def select_action(q_values, step, start, end, decay):
    # Calculate the threshold value for this step
    epsilon = end + (____) * math.exp(____ / ____)
    # Draw a random number between 0 and 1
    sample = random.____
    if sample < epsilon:
        # Return a random action index
        return random.____
    # Return the action index with highest Q-value
    return torch.____.item()
      
for step in [1, 500, 2500]:
    actions = [select_action(torch.Tensor([1, 2, 3, 5]), step, .9, .05, 1000) for _ in range(20)]
    print(f"Selecting 20 actions at step {step}.\nThe action with highest q-value is action 3.\nSelected actions: {actions}\n\n")

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Deep Reinforcement Learning in Python

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4.8+

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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 con experience replay Exercise 2: La coda a due estremità Exercise 3: Experience replay buffer Exercise 4: DQN con experience replay Exercise 5: L'algoritmo DQN completo Exercise 6: Epsilon-greediness

Esercizio in corso

Exercise 7: Q-target fissi Exercise 8: Implementare l'algoritmo DQN completo Exercise 9: Double DQN Exercise 10: Rete online e rete target in DDQN Exercise 11: Addestrare il Double DQN Exercise 12: Prioritized experience replay Exercise 13: Prioritized experience replay buffer Exercise 14: Campionare dal buffer PER Exercise 15: DQN con 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!