New exploration method boosts searchless chess AI strength

Researchers have developed a new method called prior-directed exploration for searchless chess engines, aiming to improve their performance beyond simple imitation of stronger players. This technique replaces the standard entropy bonus with a forward Kullback-Leibler divergence that guides exploration towards promising moves identified by the network's own search prior. The approach also incorporates an adaptive sampling temperature based on outcome uncertainty, leading to improved tactical accuracy and playing strength in self-play reinforcement learning. AI

IMPACT Enhances AI's ability to learn complex strategies without relying on extensive search, potentially applicable to other domains.

RANK_REASON The cluster contains an academic paper detailing a new method for AI in chess. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• alphaXiv
• AlphaZero
• CatalyzeX
• Chessformer
• DagsHub
• Gotit.pub
• Hugging Face
• Kullback–Leibler divergence
• Leela Chess Zero
• Monte Carlo tree search
• reinforcement learning
• ScienceCast

• paper
• model release

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