{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bilevel-entropy-based-mechanism-design-for","title":"Bilevel Entropy based Mechanism Design for Balancing Meta in Video Games","arxiv_id":null,"date":"2023-05-27","proceeding":"Autonomous Agents and Multi Agent Systems (AAMAS) 2023 5","authors":["Sumedh Pendurkar","Chris Chow","Luo Jie","Guni Sharon"],"abstract":"We address a mechanism design problem where the goal of the\r\ndesigner is to maximize the entropy of a player’s mixed strategy at\r\na Nash equilibrium. This objective is of special relevance to video\r\ngames where game designers wish to diversify the players’ interaction with the game. To solve this design problem, we propose a\r\nbi-level alternating optimization technique that (1) approximates\r\nthe mixed strategy Nash equilibrium using a Nash Monte-Carlo\r\nreinforcement learning approach and (2) applies a gradient-free optimization technique (Covariance-Matrix Adaptation Evolutionary\r\nStrategy) to maximize the entropy of the mixed strategy obtained in\r\nlevel (1). The experimental results show that our approach achieves\r\ncomparable results to the state-of-the-art approach on three benchmark domains “Rock-Paper-Scissors-Fire-Water”, “Workshop Warfare” and “Pokemon Video Game Championship”. Next, we show\r\nthat, unlike previous state-of-the-art approaches, the computational\r\ncomplexity of our proposed approach scales significantly better in\r\nlarger combinatorial strategy spaces.","url_abs":"https://people.engr.tamu.edu/guni/pistar/Papers/AAMAS23-meta.pdf","url_pdf":"https://people.engr.tamu.edu/guni/pistar/Papers/AAMAS23-meta.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bilevel-entropy-based-mechanism-design-for","repo_url":"https://github.com/nianticlabs/metagame-balance","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}