Calcolo dei valori-Q

Il tuo obiettivo è calcolare i valori-azione, noti anche come valori-Q, per ogni coppia stato-azione nell'ambiente personalizzato MyGridWorld seguendo la politica mostrata qui sotto. Nell'RL, i valori-Q sono fondamentali perché rappresentano l'utilità attesa dell'esecuzione di una specifica azione in un dato stato, seguita dal rispetto della politica.

L'ambiente è stato importato come env insieme alla funzione compute_state_value() e alle variabili necessarie (terminal_state, num_states, num_actions, policy, gamma).

Questo esercizio fa parte del corso

Reinforcement Learning con Gymnasium in Python

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Istruzioni dell'esercizio

Completa la funzione compute_q_value() per calcolare il valore-azione per un dato state e action.
Crea un dizionario Q in cui ogni chiave rappresenta una coppia stato-azione e il valore corrispondente è il valore-Q per quella coppia.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Complete the function to compute the action-value for a state-action pair
def compute_q_value(state, action):
    if state == terminal_state:
        return None   
    probability, next_state, reward, done = ____
    return ____

# Compute Q-values for each state-action pair
Q = {(____, ____): _____ for ____ in range(____) for ____ in range(____)}

print(Q)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Reinforcement Learning con Gymnasium in Python

AvançadoNível de habilidade

4.8+

Inizia il corso 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: Processi decisionali di Markov Exercise 2: Componenti personalizzati dell'MDP Frozen Lake Exercise 3: Esplorare gli spazi degli stati e delle azioni Exercise 4: Probabilità di transizione e ricompense Exercise 5: Policy e funzioni di valore di stato Exercise 6: Definire una policy deterministica Exercise 7: Calcolo dei valori di stato per una policy Exercise 8: Confrontare le policy Exercise 9: Funzioni valore-azione Exercise 10: Calcolo dei valori-Q

Esercizio in corso

Exercise 11: Migliorare una policy Exercise 12: Iterazione della politica e iterazione del valore Exercise 13: Applicare la policy iteration per una politica ottimale Exercise 14: Implementare la 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: 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!