Generalized Rapid Action Value Estimation in Memory-Constrained Environments
AI 摘要
提出GRAVE2、GRAVER、GRAVER2算法，减少内存占用，保持GRAVE的博弈强度。
主要贡献
- 提出了GRAVE2算法，通过两层搜索扩展GRAVE。
- 提出了GRAVER算法，利用节点回收减少内存占用。
- 提出了GRAVER2算法，结合两层搜索和节点回收。
- 实验证明新算法在降低内存占用的同时，保持了GRAVE的博弈强度。
方法论
通过两层搜索、节点回收以及二者结合的策略，在内存受限的环境下改进GRAVE算法。
原文摘要
Generalized Rapid Action Value Estimation (GRAVE) has been shown to be a strong variant within the Monte-Carlo Tree Search (MCTS) family of algorithms for General Game Playing (GGP). However, its reliance on storing additional win/visit statistics at each node makes its use impractical in memory-constrained environments, thereby limiting its applicability in practice. In this paper, we introduce the GRAVE2, GRAVER and GRAVER2 algorithms, which extend GRAVE through two-level search, node recycling, and a combination of both techniques, respectively. We show that these enhancements enable a drastic reduction in the number of stored nodes while matching the playing strength of GRAVE.