Implementación de value iteration

Value iteration es un método clave en RL para encontrar la política óptima. Mejora iterativamente la función de valor de cada estado hasta que converge, lo que permite descubrir la política óptima. Empezarás con una función de valor V y una policy ya inicializadas y cargadas. Luego, las actualizarás en un bucle hasta que la función de valor converja y verás la política en acción.

La función get_max_action_and_value(state, V) ya está precargada para ti.

Este ejercicio forma parte del curso

Reinforcement Learning con Gymnasium en Python

Instrucciones del ejercicio

Para cada estado, encuentra la acción con el valor Q máximo (max_action) y su valor correspondiente (max_q_value).
Actualiza el diccionario new_V y la policy según max_action y max_q_value.
Comprueba la convergencia verificando que la diferencia entre new_v y V para cada estado sea menor que threshold.

Ejercicio interactivo práctico

Prueba este ejercicio y completa el código de muestra.

threshold = 0.001
while True:
  new_V = {}
  for state in range(num_states-1):
    # Get action with maximum Q-value and its value 
    max_action, max_q_value = ____
    # Update the value function and policy
    new_V[state] = ____
    policy[state] = ____
  # Test if change in state values is negligeable
  if all(abs(____ - ____) < ____ for state in ____):
    break
  V = new_V
render_policy(policy)

Editar y ejecutar código

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: Procesos de decisión de Markov Exercise 2: Componentes personalizados del MDP de Frozen Lake Exercise 3: Explorar los espacios de estados y acciones Exercise 4: Probabilidades de transición y recompensas Exercise 5: Políticas y funciones de valor de estado Exercise 6: Definir una política determinista Exercise 7: Calcular valores de estado para una política Exercise 8: Comparar políticas Exercise 9: Funciones valor-acción Exercise 10: Cálculo de Q-values Exercise 11: Mejorar una política Exercise 12: Iteración de políticas e iteración de valores Exercise 13: Aplicar iteración de políticas para obtener la política óptima Exercise 14: Implementación de value iteration

Ejercicio actual

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: 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!