mlsanityData to 14 Oct 2026

Turn-based Multi-Agent Reinforcement Learning Model Checking

arXiv 2501.03187 ↗First posted 6 Jan 2025cs.AI cs.LG cs.MAcited by 3

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Abstract (arXiv)

In this paper, we propose a novel approach for verifying the compliance of turn-based multi-agent reinforcement learning (TMARL) agents with complex requirements in stochastic multiplayer games. Our method overcomes the limitations of existing verification approaches, which are inadequate for dealing with TMARL agents and not scalable to large games with multiple agents. Our approach relies on tight integration of TMARL and a verification technique referred to as model checking. We demonstrate the effectiveness and scalability of our technique through experiments in different types of environments. Our experiments show that our method is suited to verify TMARL agents and scales better than naive monolithic model checking.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 17 of its 45 references that are in the map. First posted on arXiv 6 Jan 2025.

Builds on

17 of 45 references.

References · 17

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,103
  3. 2018Omega-Regular Objectives in Model-Free Reinforcement Learningcited by 75
  4. 2017Environment-Independent Task Specifications via GLTLcited by 67
  5. 2020The Probabilistic Model Checker Stormcited by 66
  6. 2021Scalar reward is not enough: A response to Silver, Singh, Precup and Sutton (2021)cited by 55
  7. 2021Deep Multiagent Reinforcement Learning: Challenges and Directionscited by 42
  8. 2019Reinforcement Learning with Probabilistic Guarantees for Autonomous Drivingcited by 28
  9. 2022COOL-MC: A Comprehensive Tool for Reinforcement Learning and Model Checkingcited by 19
  10. 2019Verifiably Safe Off-Model Reinforcement Learningcited by 16
  11. 2020Statistically Model Checking PCTL Specifications on Markov Decision Processes via Reinforcement Learningcited by 12
  12. 2022Specification-Guided Learning of Nash Equilibria with High Social Welfarecited by 8
5 more
  1. 2016Optimizing Expectation with Guarantees in POMDPs (Technical Report)cited by 7
  2. 2021Feature-Based Interpretable Reinforcement Learning based on State-Transition Modelscited by 6
  3. 2019Learning Safe Unlabeled Multi-Robot Planning with Motion Constraintscited by 4
  4. 2022Symbolic Verification and Strategy Synthesis for Turn-based Stochastic Gamescited by 3
  5. 2019Verification and Control of Turn-Based Probabilistic Real-Time Gamescited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026COOL-MC: Verifying and Explaining RL Policies for Platelet Inventory Managementcited by 0
  2. 2026COOL-MC: Verifying and Explaining RL Policies for Multi-bridge Network Maintenancecited by 0
  3. 2026Automated Testing of LLM-Based Post Hoc Explainers Using Model Checking as an Oraclecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Probabilistic Model Checking of Stochastic Reinforcement Learning Policies
  2. 2024Efficient Learning in Chinese Checkers: Comparing Parameter Sharing in Multi-Agent Reinforcement Learning
  3. 2026RTPO: Reverse-Turn Policy Optimization for Stabilizing Agentic RL Training
  4. 2025MARSHAL: Incentivizing Multi-Agent Reasoning via Self-Play with Strategic LLMs
  5. 2026CM2: Reinforcement Learning with Checklist Rewards for Multi-Turn and Multi-Step Agentic Tool Use
  6. 2026AT2PO: Agentic Turn-based Policy Optimization via Tree Search
  7. 2026A Survey on the Verification of Reinforcement Learning Policies
  8. 2023A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem