Robotic Strategy Adaptation via Monte Carlo Regret Minimization in Uncertain Multi-Agent Environments

Robotic Strategy Adaptation via Monte Carlo Regret Minimization in Uncertain Multi-Agent Environments | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 18 March 2026 V1 Latest version Share on Robotic Strategy Adaptation via Monte Carlo Regret Minimization in Uncertain Multi-Agent Environments Author : Sushant Shivankar 0009-0002-8892-6889 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177385893.32317660/v1 101 views 41 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This paper introduces a novel algorithmic framework that adapts Monte Carlo Counterfactual Regret Minimization (MCCFR) to real-time robotic decision-making in dynamic, imperfect-information scenarios. By integrating incremental tree search and targeted sampling, our method enables autonomous agents-such as multi-robot systems operating under partial observability-to compute near-equilibrium strategies online with limited computational resources. We demonstrate the approach's convergence guarantees in simulation-based adversarial settings, where robotic agents must conceal private sensor data while inferring opponent intent. Experimental results in simulated tactical pursuit-evasion and distributed resource competition games confirm that our algorithm reduces exploitability over time and outperforms existing imperfect-information search methods, providing a principled foundation for robust robotic interaction under uncertainty. Supplementary Material File (robotic_strategy_adaptation_via_monte_carlo_regret_minimization_in_uncertain_multi_agent_environments.pdf) Download 199.70 KB Information & Authors Information Version history V1 Version 1 18 March 2026 Copyright This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License Keywords exploitability reduction / near-equilibrium strategies imperfect-information game-theoretic search monte carlo counterfactual regret minimization (mccfr) multi-agent robotic decision-making under partial observability online outcome sampling (oos) Authors Affiliations Sushant Shivankar 0009-0002-8892-6889 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 101 views 41 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Sushant Shivankar. Robotic Strategy Adaptation via Monte Carlo Regret Minimization in Uncertain Multi-Agent Environments. Authorea . 18 March 2026. DOI: https://doi.org/10.22541/au.177385893.32317660/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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