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Combining Model Checking and Reinforcement Learning for Scalable Mission Planning of Autonomous Agents

Fulltext:


Publication Type:

Report - MRTC

Publisher:

Mälardalen Real-Time Research Centre, Mälardalen University

ISRN:

MDH-MRTC-330/2020-1-SE


Abstract

The problem of mission planning for multiple autonomous agents, including path planning and task scheduling, is often complex. Recent efforts aiming at solving this problem have explored ways of using formal methods to synthesize mission plans that satisfy various requirements. However, when the number of agents grows or requirements include real-time constraints, the complexity of the problem increases to the extent that current algorithmic formal methods cannot handle. In this paper, we propose a novel approach called MCRL, which overcomes this shortcoming by integrating model checking and reinforcement learning techniques. Our approach employs timed automata and timed computation tree logic to describe the autonomous agents' behavior and requirements, and trains the model by a reinforcement learning algorithm, namely Q-learning, to populate a table used to restrict the state space of the model. MCRL combines the ability of model checking to synthesize verifiable mission plans, and the exploration and exploitation capabilities of Q-learning to alleviate the state-space-explosion problem of exhaustive model checking. Our method provides a means to synthesize mission plans for autonomous systems whose complexity exceeds the scalability boundaries of exhaustive model checking, but also to analyze and verify synthesized mission plans in order to ensure given requirements. We evaluate the proposed method on various relevant scenarios involving autonomous agents, and also present and discuss comparisons with other methods and tools.

Bibtex

@techreport{Gu5782,
author = {Rong Gu and Eduard Paul Enoiu and Cristina Seceleanu and Kristina Lundqvist},
title = {Combining Model Checking and Reinforcement Learning for Scalable Mission Planning of Autonomous Agents},
month = {May},
year = {2020},
publisher = {M{\"a}lardalen Real-Time Research Centre, M{\"a}lardalen University},
url = {http://www.es.mdh.se/publications/5782-}
}