You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact

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


Publication Type:

Report - MRTC


Mälardalen Real-Time Research Centre, Mälardalen University




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.


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 = {}