Definir la función epsilon-greedy

En RL, la estrategia epsilon-greedy busca equilibrar la exploración y la explotación. Este método elige una acción aleatoria con probabilidad epsilon y la mejor acción conocida con probabilidad 1-epsilon. Implementar la función epsilon_greedy() es clave en algoritmos como Q-learning y SARSA, ya que facilita el aprendizaje del agente asegurando tanto la exploración del entorno como la explotación de las recompensas conocidas. Ese será el objetivo de este ejercicio.

La biblioteca numpy se ha importado como np.

Este ejercicio forma parte del curso

Reinforcement Learning con Gymnasium en Python

Instrucciones del ejercicio

Dentro de la función, escribe la condición adecuada para que el agente explore el entorno.
Elige una action aleatoria cuando explore.
Elige la mejor action según la q_table cuando explote.

Ejercicio interactivo práctico

Prueba este ejercicio y completa el código de muestra.

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

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Este ejercicio forma parte del curso

Reinforcement Learning con Gymnasium en Python

AvanzadoNivel de habilidad

4.8+

Comienza el curso 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: 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: SARSA esperado Exercise 2: Regla de actualización de Expected SARSA Exercise 3: Aplicar Expected SARSA Exercise 4: Double Q-learning Exercise 5: Implementar la regla de actualización de Double Q-learning Exercise 6: Aplicar Double Q-learning Exercise 7: Equilibrar exploración y explotación Exercise 8: Definir la función epsilon-greedy

Ejercicio actual

Exercise 9: Resolver CliffWalking con la estrategia epsilon-greedy Exercise 10: Resolver CliffWalking con una estrategia epsilon-greedy decreciente Exercise 11: Bandidos de varios brazos Exercise 12: Creando un multi-armed bandit Exercise 13: Resolver un bandido de varios brazos Exercise 14: Evaluar la convergencia en un problema de multi-armed bandit Exercise 15: ¡Enhorabuena!