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 language | English |
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Pages (from-to) | 2134-2142 |
Number of pages | 9 |
Journal | Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS |
Volume | 2023-May |
State | Published - 1 Jan 2023 |
Externally published | Yes |
Event | 22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023 - London, United Kingdom Duration: 29 May 2023 → 2 Jun 2023 |
Keywords
- Game Meta Balance
- Mechanism Design
ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Control and Systems Engineering