Exploration Policies for On-the-Fly Controller Synthesis: A Reinforcement Learning Approach

• URL: http://arxiv.org/abs/2210.05393v2
• Date: Wed, 3 May 2023 20:23:36 GMT
• Title: Exploration Policies for On-the-Fly Controller Synthesis: A Reinforcement Learning Approach
• Authors: Tom\'as Delgado, Marco S\'anchez Sorondo, V\'ictor Braberman, Sebasti\'an Uchitel
• Abstract summary: We propose a new method for obtaining unboundeds based on Reinforcement Learning (RL) Our agents learn from scratch in a highly observable partially RL task and outperform existing overall, in instances unseen during training.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Controller synthesis is in essence a case of model-based planning for non-deterministic environments in which plans (actually ''strategies'') are meant to preserve system goals indefinitely. In the case of supervisory control environments are specified as the parallel composition of state machines and valid strategies are required to be ''non-blocking'' (i.e., always enabling the environment to reach certain marked states) in addition to safe (i.e., keep the system within a safe zone). Recently, On-the-fly Directed Controller Synthesis techniques were proposed to avoid the exploration of the entire -and exponentially large-environment space, at the cost of non-maximal permissiveness, to either find a strategy or conclude that there is none. The incremental exploration of the plant is currently guided by a domain-independent human-designed heuristic. In this work, we propose a new method for obtaining heuristics based on Reinforcement Learning (RL). The synthesis algorithm is thus 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 that abstracts both states and actions, we show that it is possible to learn heuristics on small versions of a problem that generalize to the larger instances, effectively doing zero-shot policy transfer. Our agents learn from scratch in a highly partially observable RL task and outperform the existing heuristic overall, in instances unseen during training.

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