Generalized Rapid Action Value Estimation in Memory-Constrained Environments
#GRAVE algorithm#Monte-Carlo Tree Search#General Game Playing#Memory-constrained environments#Artificial Intelligence#Algorithm optimization#Node recycling#Two-level search
📌 Key Takeaways
Researchers developed three new algorithms to address memory limitations in GRAVE
The new algorithms reduce memory usage while maintaining playing strength
GRAVE2 implements two-level search, GRAVER uses node recycling, and GRAVER2 combines both approaches
The breakthrough expands practical applications of Monte-Carlo Tree Search in memory-constrained environments
📖 Full Retelling
Researchers Aloïs Rautureau, Tristan Cazenave, and Éric Piette introduced new GRAVE2, GRAVER, and GRAVER2 algorithms in their paper submitted to arXiv on February 26, 2026, addressing the memory limitations of the original Generalized Rapid Action Value Estimation algorithm used in Monte-Carlo Tree Search for General Game Playing. The paper presents three innovative approaches that extend the capabilities of GRAVE while significantly reducing its memory footprint. While GRAVE has demonstrated strong performance as a variant within the Monte-Carlo Tree Search family for General Game Playing, its practical implementation has been hindered by the need to store additional win/visit statistics at each node, making it unsuitable for memory-constrained environments. The researchers propose three distinct solutions: GRAVE2, which implements a two-level search approach; GRAVER, which introduces node recycling techniques; and GRAVER2, which combines both approaches to achieve maximum efficiency. According to the paper, these enhancements enable a drastic reduction in the number of stored nodes while maintaining the same playing strength as the original GRAVE algorithm, potentially revolutionizing how game-playing AI systems can operate on resource-limited devices.
Ability of artificial intelligence to play different games
General game playing (GGP) is the design of artificial intelligence programs to be able to play more than one game successfully. For many games like chess, computers are programmed to play these games using a specially designed algorithm, which cannot be transferred to another context. For instance,...
# Artificial Intelligence (AI)
**Artificial Intelligence (AI)** is a specialized field of computer science dedicated to the development and study of computational systems capable of performing tasks typically associated with human intelligence. These tasks include learning, reasoning, problem-solvi...
--> Computer Science > Artificial Intelligence arXiv:2602.23318 [Submitted on 26 Feb 2026] Title: Generalized Rapid Action Value Estimation in Memory-Constrained Environments Authors: Aloïs Rautureau , Tristan Cazenave , Éric Piette View a PDF of the paper titled Generalized Rapid Action Value Estimation in Memory-Constrained Environments, by Alo\"is Rautureau and 2 other authors View PDF HTML Abstract: Generalized Rapid Action Value Estimation has been shown to be a strong variant within the Monte-Carlo Tree Search family of algorithms for General Game Playing . 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.23318 [cs.AI] (or arXiv:2602.23318v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.23318 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Aloïs Rautureau [ view email ] [v1] Thu, 26 Feb 2026 18:25:59 UTC (338 KB) Full-text links: Access Paper: View a PDF of the paper titled Generalized Rapid Action Value Estimation in Memory-Constrained Environments, by Alo\"is Rautureau and 2 other authors View PDF HTML TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-02 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (...
