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Evaluating Variance-Aware PUCT in Neural Chess Engines

Ahlqvist, Axel LU and Näslund Cuesta, Oscar LU (2026) EDAN70 20261
Department of Computer Science
Abstract (Swedish)
Game-playing algorithms often face a trade-off between analyzing moves that currently look promising and exploring alternatives whose value is uncertain. AlphaZero-style chess engines combine neural network evaluation with Monte Carlo tree search to selectively explore the game tree. We evaluate a variance-aware modification of the PUCT tree policy on chess puzzles and find that it underperforms standard PUCT across different networks and search budgets under certain conditions. We suggest explanations for this discrepancy and possible improvements to the implementation of the variance-aware tree policy.
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author
Ahlqvist, Axel LU and Näslund Cuesta, Oscar LU
supervisor
organization
course
EDAN70 20261
year
type
L3 - Miscellaneous, Projetcs etc.
subject
keywords
Monte Carlo Tree Search, PUCT, chess engine, AlphaZero, reinforcement learning
language
English
id
9249101
date added to LUP
2026-08-26 10:40:24
date last changed
2026-08-26 10:50:32
@misc{9249101,
  abstract     = {{Game-playing algorithms often face a trade-off between analyzing moves that currently look promising and exploring alternatives whose value is uncertain. AlphaZero-style chess engines combine neural network evaluation with Monte Carlo tree search to selectively explore the game tree. We evaluate a variance-aware modification of the PUCT tree policy on chess puzzles and find that it underperforms standard PUCT across different networks and search budgets under certain conditions. We suggest explanations for this discrepancy and possible improvements to the implementation of the variance-aware tree policy.}},
  author       = {{Ahlqvist, Axel and Näslund Cuesta, Oscar}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Evaluating Variance-Aware PUCT in Neural Chess Engines}},
  year         = {{2026}},
}