De architectuur van het policy-netwerk

Bouw de architectuur voor een Policy Network dat je later kunt gebruiken om je policy gradient-agent te trainen.

Het policy-netwerk neemt de toestand (state) als input en geeft in de actieruimte een waarschijnlijkheid terug. In de Lunar Lander-omgeving werk je met vier discrete acties, dus je wilt dat je netwerk voor elk van die acties een waarschijnlijkheid uitstuurt.

Deze oefening maakt deel uit van de cursus

Deep Reinforcement Learning in Python

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Oefeninstructies

Geef de grootte op voor de outputlaag van het policy-netwerk; gebruik voor flexibiliteit de variabelenaam in plaats van het daadwerkelijke getal.
Zorg dat de laatste laag waarschijnlijkheden teruggeeft.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

class PolicyNetwork(nn.Module):
  def __init__(self, state_size, action_size):
    super(PolicyNetwork, self).__init__()
    self.fc1 = nn.Linear(state_size, 64)
    self.fc2 = nn.Linear(64, 64)
    # Give the desired size for the output layer
    self.fc3 = nn.Linear(64, ____)

  def forward(self, state):
    x = torch.relu(self.fc1(torch.tensor(state)))
    x = torch.relu(self.fc2(x))
    # Obtain the action probabilities
    action_probs = ____(self.fc3(x), dim=-1)
    return action_probs
  
policy_network = PolicyNetwork(8, 4)
action_probs = policy_network(state)
print('Action probabilities:', action_probs)

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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 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: Introductie tot policy gradient Exercise 2: De architectuur van het policy-netwerk

Huidige oefening

Exercise 3: Werken met discrete verdelingen Exercise 4: Policy gradient en REINFORCE Exercise 5: Actieselectie in REINFORCE Exercise 6: De REINFORCE-algoritme trainen Exercise 7: Advantage Actor-Critic Exercise 8: Critic-netwerk Exercise 9: Actor-Critic: verliesberekeningen Exercise 10: Het A2C-algoritme trainen

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!