Generalized Rapid Action Value Estimation in Memory-Constrained Environments
(2026) Computer and Games 2026 — Location: Maastricht (19.June.2026)
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- Abstract
- 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 GRAVE 2 , GRAVER and GRAVER 2 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.
- Affiliations
Citations
Alois Rautureau, Tristan Cazenave, & Piette, E. (2026). Generalized Rapid Action Value Estimation in Memory-Constrained Environments. In Dennis Soemers, Eric Piette (ed.), Computer and Games 2026. https://hdl.handle.net/2078.5/278553