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Approximate Value Iteration proves surprisingly effective in AI game-playing

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the effectiveness of Approximate Value Iteration (AVI) in self-play for game-playing programs. Contrary to expectations, AVI demonstrated surprising effectiveness, learning more accurate value functions than AlphaZero and maintaining competitive policies at significantly lower computational costs. The study suggests that simpler approaches like AVI may have been overshadowed by more complex methods such as Monte Carlo Tree Search (MCTS), despite their increasing practicality with modern deep learning tools. AI

IMPACT Suggests simpler AI algorithms may be competitive with complex methods like MCTS for game-playing, potentially reducing computational costs.

RANK_REASON The cluster contains a research paper detailing a new approach to game-playing algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Approximate Value Iteration proves surprisingly effective in AI game-playing

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The cluster contains a research paper detailing a new approach to game-playing algorithms. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Raphael Boige, Amine Boumaza, Bruno Scherrer ·

    The Surprising Effectiveness of Approximate Value Iteration in Self-Play

    arXiv:2609.09094v1 Announce Type: new Abstract: 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 C…

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