Research map / Learning theory and algorithms / Combinatorial and search optimization
Memory-Augmented Monte Carlo Tree Search
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use small temperature tau=0.05 in memory weighting (ablation) | M-MCTS · Go vs baseline | win rate | Hurt |
| increase neighbours M from 50 to 100 (ablation) | M-MCTS · Go vs baseline | win rate | Hurt |
| reduce memory size to 1000 from 10000 (ablation) | M-MCTS · Go vs baseline | win rate | Hurt |
| add memory-based value approximation to MCTS | Fuego + DCNN · Go vs baseline, 10,000 sims | win rate: 71% win rate | Helped |
| add memory-based value approximation under equal time | Fuego + DCNN · Go vs baseline, 5 s per move | win rate: 61% win rate | Helped |
Closest papers by meaning
- Monte Carlo Tree Search in the Presence of Transition Uncertainty (AAAI 2024 · similarity 0.89)
- Speculative Monte-Carlo Tree Search (NeurIPS 2024 · similarity 0.89)
- Monte-Carlo Tree Search with Uncertainty Propagation via Optimal Transport (ICML 2025 · similarity 0.87)
- An Efficient Algorithm for Thresholding Monte Carlo Tree Search (NeurIPS 2026 · similarity 0.87)
- Graph-Based Stochastic-Power-UCT: Monte-Carlo Graph Search with Power Mean Estimation (NeurIPS 2026 · similarity 0.86)
- Power Mean Estimation in Stochastic Continuous Monte-Carlo Tree Search (ICML 2025 · similarity 0.86)
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