Bilevel Entropy based Mechanism Design for Balancing Meta in Video Games

Sumedh Pendurkar, Chris Chow, Luo Jie, Guni Sharon

Research output: Contribution to journalConference articlepeer-review

Abstract

We address a mechanism design problem where the goal of the designer is to maximize the entropy of a player's mixed strategy at a Nash equilibrium. This objective is of special relevance to video games where game designers wish to diversify the players' interaction with the game. To solve this design problem, we propose a bi-level alternating optimization technique that (1) approximates the mixed strategy Nash equilibrium using a Nash Monte-Carlo reinforcement learning approach and (2) applies a gradient-free optimization technique (Covariance-Matrix Adaptation Evolutionary Strategy) to maximize the entropy of the mixed strategy obtained in level (1). The experimental results show that our approach achieves comparable 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 that, unlike previous state-of-the-art approaches, the computational complexity of our proposed approach scales significantly better in larger combinatorial strategy spaces.

Original languageEnglish
Pages (from-to)2134-2142
Number of pages9
JournalProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume2023-May
StatePublished - 1 Jan 2023
Externally publishedYes
Event22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023 - London, United Kingdom
Duration: 29 May 20232 Jun 2023

Keywords

  • Game Meta Balance
  • Mechanism Design

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

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