Correctness-Guaranteed Strategy Synthesis and Compression for Multi-Agent Autonomous Systems

Abstract

Planning is a critical function of multi-agent autonomous systems, which includes path finding and task scheduling. Exhaustive search-based methods such as model checking and algorithmic game theory can solve simple instances of multi-agent planning. However, these methods suffer from the state-space explosion when the number of agents is large. Learning-based methods can alleviate this problem but lack a guarantee of the correctness of the results. In this paper, we introduce MoCReL, a new version of our previously proposed method that combines model checking with reinforcement learning in solving the planning problem. The approach takes advantage of reinforcement learning to synthesize path plans and task schedules for large numbers of autonomous agents, and of model checking to verify the correctness of the synthesized strategies. Further, MoCReL can compress large strategies into smaller ones that have down to 0.05% of the original sizes, while preserving their correctness, which we show in this paper. MoCReL is integrated into a new version of UPPAAL Stratego that supports calling external libraries when running learning and verification of timed games models.

Keywords: Planning, Multi-Agent Autonomous Systems, Timed Games, Reinforcement Learning, Strategy Compression