SOURCE-LINKED INTELLIGENCE

The Surprising Effectiveness of Approximate Value Iteration in Self-Play

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

Combining search with function approximation has driven major advances in game-playing programs, making self-play algorithms more competitive than ever. Still, the computational overhead of the most popular methods, based on Monte Carlo Tree Search (MCTS), can be substantial. In this work, we investigate whether simpler methods remain competitive in non-trivial, moderately sized games such as Connect Four, Hex(7x7) and synthetic games. We train a minimal self-play implementation of Approximate Value Iteration (AVI) and use ground-truth oracles for exact evaluation. Contrary to expectations, ou

Read original source ↗ Open in workspace

recordType

paper

region

Global