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Scaling Directed Controller Synthesis via Reinforcement Learning

2022-10-07 20:28:25
Tomás Delgado, Víctor Braberman, Sebastian Uchitel
arXiv_AI
         
Abstract

Directed Controller Synthesis technique finds solutions for the non-blocking property in discrete event systems by exploring a reduced portion of the exponentially big state space, using best-first search. Aiming to minimize the explored states, it is currently guided by a domain-independent handcrafted heuristic, with which it reaches state-of-the-art performance. In this work, we propose a new method for obtaining heuristics based on Reinforcement Learning. The synthesis algorithm is framed as an RL task with an unbounded action space and a modified version of DQN is used. With a simple and general set of features, we show that it is possible to learn heuristics on small versions of a problem in a way that generalizes to the larger instances. Our agents learn from scratch and outperform the existing heuristic overall, in instances unseen during training.

Abstract (translated)

URL

https://arxiv.org/abs/2210.05393

PDF

https://arxiv.org/pdf/2210.05393.pdf

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