GRAVE算法在内存受限环境下难以应用，新方法通过双层搜索和节点复用显著减少存储需求。
Generalized Rapid Action Value Estimation in Memory-Constrained Environments
- 采用双层搜索与节点复用机制，降低内存占用
- 仅需少量存储节点即达到GRAVE同等博弈水平
- 适合资源受限的实时游戏系统部署
广义快速动作价值估计（GRAVE）是通用游戏博弈中蒙特卡洛树搜索（MCTS）算法的强有力变体。然而，其在每个节点存储额外胜负/访问统计信息的特性，使其在内存受限环境中难以应用，限制了实际使用。本文提出GRAVE2、GRAVER和GRAVER2三种算法，分别通过两层搜索、节点回收以及两者结合的方式扩展GRAVE。实验表明，这些改进可在大幅减少存储节点数量的同时，保持与GRAVE相当的博弈能力。
原文摘要 · Abstract (English)
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.
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