M-Learning: Heuristic Approach for Delayed Rewards in Reinforcement Learning

M-Learning: Heuristic Approach for Delayed Rewards in Reinforcement Learning

The current design of reinforcement learning methods requires extensive computational resources. Algorithms such as Deep Q-Network (DQN) have obtained outstanding results in advancing the field. However, the need to tune thousands of parameters and run millions of training episodes remains a signifi...

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Bibliographic Details
Main Authors: Cesar Andrey Perdomo Charry, Marlon Sneider Mora Cortes, Oscar J. Perdomo
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Mathematics
Subjects:
reinforcement learning
exploration–exploitation dilemma
Q-learning
frozen lake
heuristic approach
Online Access:https://www.mdpi.com/2227-7390/13/13/2108
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