Generalized Rapid Action Value Estimation in Memory-Constrained Environments

Aloïs Rautureau; Tristan Cazenave; Éric Piette

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arXiv · 2602.23318

Generalized Rapid Action Value Estimation in Memory-Constrained Environments

Aloïs Rautureau·Tristan Cazenave·Éric Piette

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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 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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Aloïs Rautureau, Tristan Cazenave, Éric Piette. 2026-02-26. Generalized Rapid Action Value Estimation in Memory-Constrained Environments. https://arxiv.org/abs/2602.23318

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