Epsilon-greedy fonksiyonunu tanımlama

RL'de epsilon-greedy stratejisi, keşif ve sömürü (exploration ve exploitation) arasında bir dengedir. Bu yöntem, epsilon olasılığıyla rastgele bir eylem ve 1-epsilon olasılığıyla bilinen en iyi eylemi seçer. epsilon_greedy() fonksiyonunu uygulamak, Q-learning ve SARSA gibi algoritmalar için kritiktir; ajanın hem ortamı keşfetmesini hem de bilinen ödüllerden yararlanmasını sağlayarak öğrenme sürecini kolaylaştırır. Bu egzersizin amacı da bunu gerçekleştirmektir.

numpy kütüphanesi np olarak içe aktarılmıştır.

Bu egzersiz

Python ile Gymnasium'da Reinforcement Learning

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Fonksiyonun içinde, ajanın ortamı keşfetmesi için uygun koşulu yaz.
Keşif yaparken rastgele bir action seç.
Sömürü yaparken q_table'a göre en iyi action'ı seç.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

epsilon = 0.2
env = gym.make('FrozenLake')
q_table = np.random.rand(env.observation_space.n, env.action_space.n)

def epsilon_greedy(state):
    # Implement the condition to explore
    if ____ < ____:
      	# Choose a random action
        action = ____
    else:
      	# Choose the best action according to q_table
        action = ____
    return action

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 methods Exercise 2: Episode generation for Monte Carlo methods Exercise 3: Implementing first-visit Monte Carlo Exercise 4: Implementing every-visit Monte Carlo Exercise 5: Temporal difference learning Exercise 6: Implementing the SARSA update rule Exercise 7: Solving 8x8 Frozen Lake with SARSA Exercise 8: Q-learning Exercise 9: Implementing Q-learning update rule Exercise 10: Solving 8x8 Frozen Lake with Q-learning Exercise 11: Evaluating policy on a slippery 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: Beklenen SARSA Exercise 2: Expected SARSA güncelleme kuralı Exercise 3: Expected SARSA'yı uygulamak Exercise 4: Double Q-learning Exercise 5: Double Q-learning güncelleme kuralını uygulama Exercise 6: Double Q-learning uygulama Exercise 7: Keşif ve sömürüyü dengelemek Exercise 8: Epsilon-greedy fonksiyonunu tanımlama

Geçerli Egzersiz

Exercise 9: Epsilon greedy stratejisiyle CliffWalking çözümü Exercise 10: Azalan epsilon-greedy stratejisiyle CliffWalking'i çözme Exercise 11: Çok kollu haydutlar Exercise 12: Çok kollu bir bandit oluşturma Exercise 13: Çok kollu haydut çözme Exercise 14: Çok kollu bir banditte yakınsamayı değerlendirme Exercise 15: Tebrikler!