Papers › Bilevel Entropy based Mechanism Design for Balancing Meta in Video Games
Bilevel Entropy based Mechanism Design for Balancing Meta in Video Games
Sumedh Pendurkar, Chris Chow, Luo Jie, Guni Sharon
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections