Her-ziyaret Monte Carlo'yu uygulama

Every-Visit Monte Carlo yöntemi, First-Visit varyantından farklı olarak, bir durum-eylem çifti her göründüğünde değerleri günceller; yalnızca ilk karşılaşmada değil. Bu yaklaşım, bölümlerden gelen tüm bilgileri kullanarak politikanın kapsamlı bir değerlendirmesini sağlarken, bölüm içinde ne zaman ortaya çıktıklarına bakılmaksızın tüm örnekleri dahil ettiği için değer tahminlerinde daha fazla varyans da oluşturabilir. Görevin, num_episodes bölüm boyunca eylem-değer fonksiyonu Q'yu tahmin eden every_visit_mc() fonksiyonunun uygulamasını tamamlamak.

Durum-eylem çiftleri anahtar olacak şekilde başlatılmış ve önceden yüklenmiş returns_sum ve returns_count sözlükleri ile generate_episode() fonksiyonu sana sağlandı.

Bu egzersiz

Python ile Gymnasium'da Reinforcement Learning

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

generate_episode() fonksiyonunu kullanarak bir bölüm üret.
Bir bölüm içindeki her durum-eylem çifti için getirileri ve sayılarını güncelle.
Tahmini Q-değerlerini hesapla.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

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())

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile Gymnasium'da Reinforcement Learning

kursunun bir parçasıdır

AvançadoNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: Monte Carlo yöntemleri Exercise 2: Monte Carlo yöntemleri için bölüm (episode) üretimi Exercise 3: First-visit Monte Carlo’yu uygulama Exercise 4: Her-ziyaret Monte Carlo'yu uygulama

Geçerli Egzersiz

Exercise 5: Zamansal fark öğrenimi Exercise 6: SARSA güncelleme kuralını uygulama Exercise 7: SARSA ile 8x8 Frozen Lake çözümü Exercise 8: Q-learning Exercise 9: Q-learning güncelleme kuralını uygulama Exercise 10: Q-learning ile 8x8 Frozen Lake'i Çözme Exercise 11: Kaygan Frozen Lake üzerinde politikayı değerlendirme

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!