Bilevel Entropy based Mechanism Design for Balancing Meta in Video Games
Bilevel Entropy based Mechanism Design for Balancing Meta in Video Games
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DOI：
10.5555/3545946.3598887
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Sumedh Pendurkar;Chris Chow;Luo Jie;Guni Sharon
中科院分区：
文献类型：
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作者：
Sumedh Pendurkar;Chris Chow;Luo Jie;Guni Sharon
我们解决的机制设计问题，设计者的目标是最大限度地提高熵的球员的混合策略在纳什均衡。这一目标对于游戏设计者希望使玩家与游戏的互动多样化的视频游戏特别相关。为了解决这个设计问题，我们提出了一个双层交替优化技术，（1）使用纳什蒙特-卡罗强化学习方法来近似混合策略纳什均衡，（2）应用无梯度优化技术（协方差矩阵自适应进化策略）来最大化在级别（1）中获得的混合策略的熵。实验结果表明，我们的方法取得了相当的结果，国家的最先进的方法在三个基准领域“石头-纸-剪刀-火-水”，“车间战争”和“口袋妖怪视频游戏锦标赛”。接下来，我们表明，与以前的国家的最先进的方法，我们提出的方法的计算复杂性的规模显着更好地在更大的组合策略空间。
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’ inter-action 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 War-fare” 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.