Belief-Guided Decision Making with Uncertainty Gating in the Game of Go
Quick Answer
The paper presents a Belief-Guided architecture for Go that separates the Policy and Belief heads, enhancing performance on consumer hardware by reducing reliance on MCTS.
Quick Take
This model significantly improves win rates and reduces hallucination, enabling professional-level play without extensive computational resources.
Key Points
- Introduces Belief-Guided architecture to improve Go decision-making.
- Separates Policy head from Belief head for better uncertainty modeling.
- Integrates memory mechanisms to manage long-term dependencies effectively.
- Achieves significant search-free win rates on consumer-grade hardware.
- Reduces hallucination in move proposals, enhancing strategic stability.
Paper Resources
Source Excerpt
Recent advancements in Computer Go, driven by AlphaZero and MuZero, rely heavily on Monte Carlo Tree Search (MCTS) to correct the errors of the neural network policy. While effective on massive computational clusters, this dependence creates a critical bottleneck on consumer-grade hardware, where the computational cost of tree management severely limits inference rates. Furthermore, without deep search, these models suffer from hallucination, proposing moves with high confidence that are strateg
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