Every-Visit Monte Carlo implementeren

De Every-Visit Monte Carlo-methode verschilt van de First-Visit-variant doordat waarden elke keer worden bijgewerkt wanneer een status-actiepair voorkomt, in plaats van alleen bij de eerste keer. Deze aanpak geeft een volledige evaluatie van het beleid doordat alle beschikbare informatie uit de episodes wordt gebruikt, maar kan ook meer variantie in de waarde-inschattingen introduceren omdat alle steekproeven worden meegenomen, ongeacht wanneer ze in de episode plaatsvinden. Je taak is om de implementatie van de functie every_visit_mc() te voltooien, die de actie-waardefunctie Q schat over num_episodes episodes.

De woordenboeken returns_sum en returns_count, met status-actieparen als sleutels, zijn voor je geïnitialiseerd en vooraf geladen, samen met de functie generate_episode().

Deze oefening maakt deel uit van de cursus

Reinforcement Learning met Gymnasium in Python

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Oefeninstructies

Genereer een episode met de functie generate_episode().
Werk de returns en hun tellingen bij voor elk status-actiepair binnen een episode.
Bereken de geschatte Q-waarden.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

Q = np.zeros((num_states, num_actions))
for i in range(100):
  # Generate an episode
  episode = ____
  # Update the returns and their counts
  for j, (state, action, reward) in ____:
    returns_sum[(state,  action)] += sum(____)
    returns_count[(state,  action)] += ____

# Update the Q-values for visited state-action pairs 
nonzero_counts = ____
Q[nonzero_counts] = ____
    
render_policy(get_policy())

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Reinforcement Learning met Gymnasium in Python

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4.8+

Begin de cursus gratis

Dive into the exciting world of Reinforcement Learning (RL) by exploring its foundational concepts, roles, and applications. Navigate through the RL framework, uncovering the agent-environment interaction. You'll also learn how to use the Gymnasium library to create environments, visualize states, and perform actions, thus gaining a practical foundation in RL concepts and applications.

Exercise 1: Fundamentals of reinforcement learning Exercise 2: What is Reinforcement Learning?Exercise 3: RL vs. other ML sub-domains Exercise 4: Scenarios for applying RL Exercise 5: Navigating the RL framework Exercise 6: RL interaction loop Exercise 7: Episodic and continuous RL tasks Exercise 8: Calculating discounted returns for agent strategies Exercise 9: Interacting with Gymnasium environments Exercise 10: Setting up a Mountain Car environment Exercise 11: Visualizing the Mountain Car Environment Exercise 12: Interacting with the Frozen Lake environment

Delve deeper into the world of RL focusing on model-based learning. Unravel the complexities of Markov Decision Processes (MDPs), understanding their essential components. Enhance your skill set by learning about policies and value functions. Gain expertise in policy optimization with policy iteration and value Iteration techniques.

Exercise 1: Markov Decision Processes Exercise 2: Custom Frozen Lake MDP components Exercise 3: Exploring state and action spaces Exercise 4: Transition probabilities and rewards Exercise 5: Policies and state-value functions Exercise 6: Defining a deterministic policy Exercise 7: Computing state-values for a policy Exercise 8: Comparing policies Exercise 9: Action-value functions Exercise 10: Computing Q-values Exercise 11: Improving a policy Exercise 12: Policy iteration and value iteration Exercise 13: Applying policy iteration for optimal policy Exercise 14: Implementing value iteration

Embark on a journey through the dynamic realm of Model-Free Learning in RL. Get introduced to to the foundational Monte Carlo methods, and apply first-visit and every-visit Monte Carlo prediction algorithms. Transition into the world of Temporal Difference Learning, exploring the SARSA algorithm. Finally, dive into the depths of Q-Learning, and analyze its convergence in challenging environments.

Exercise 1: Montecarlo-methoden Exercise 2: Episodes genereren voor Monte Carlo-methoden Exercise 3: First-visit Monte Carlo implementeren Exercise 4: Every-Visit Monte Carlo implementeren

Huidige oefening

Exercise 5: Temporal-differentieleren Exercise 6: De SARSA-update regel implementeren Exercise 7: 8x8 Frozen Lake oplossen met SARSA Exercise 8: Q-learning Exercise 9: Q-learning-bijwerkregel implementeren Exercise 10: 8x8 Frozen Lake oplossen met Q-learning Exercise 11: Beleid evalueren op een glad Frozen Lake

Dive into advanced strategies in Model-Free RL, focusing on enhancing decision-making algorithms. Learn about Expected SARSA for more accurate policy updates and Double Q-learning to mitigate overestimation bias. Explore the Exploration-Exploitation Tradeoff, mastering epsilon-greedy and epsilon-decay strategies for optimal action selection. Tackle the Multi-Armed Bandit Problem, applying strategies to solve decision-making challenges under uncertainty.

Exercise 1: Expected SARSA Exercise 2: Expected SARSA update rule Exercise 3: Applying Expected SARSA Exercise 4: Double Q-learning Exercise 5: Implementing double Q-learning update rule Exercise 6: Applying double Q-learning Exercise 7: Balancing exploration and exploitation Exercise 8: Defining epsilon-greedy function Exercise 9: Solving CliffWalking with epsilon greedy strategy Exercise 10: Solving CliffWalking with decayed epsilon-greedy strategy Exercise 11: Multi-armed bandits Exercise 12: Creating a multi-armed bandit Exercise 13: Solving a multi-armed bandit Exercise 14: Assessing convergence in a multi-armed bandit Exercise 15: Congratulations!