Adaptive Reinforcement Learning for State Avoidance in Discrete Event Systems
Abstract
Reinforcement learning (RL) has emerged as a potent paradigm for autonomous decision-making in complex environments. However, the integration of event-driven decision processes within RL remains a challenge. This paper presents a novel architecture that combines a Discrete Event Supervisory (DES) model with a standard RL framework to create a hybrid decision-making system. Our model leverages the DES's capabilities in managing event-based dynamics with the RL agent's adaptability to continuous states and actions, facilitating a more robust and flexible control strategy in systems characterized by both continuous and discrete events. The DES model operates alongside the RL agent, enhancing the policy's performance with event-based insights, while the environment's state transitions are governed by a mechanistic model. We demonstrate the efficacy of our approach through simulations that show improved performance metrics over traditional RL implementations. Our results suggest that this integrated approach holds promise for applications ranging from industrial automation to intelligent traffic systems, where discrete event handling is paramount.
Cite
@article{arxiv.2503.00192,
title = {Adaptive Reinforcement Learning for State Avoidance in Discrete Event Systems},
author = {Md Nur-A-Adam Dony},
journal= {arXiv preprint arXiv:2503.00192},
year = {2025}
}
Comments
This submission was made prematurely and without obtaining the appropriate permissions from all individuals initially listed. I now recognize that the submission did not meet the standards of authorship or originality expected for preprints. I am withdrawing it out of respect for academic integrity and to ensure that all future work is submitted in accordance with proper ethical guidelines