Research map / Multi-agent systems and games / Games and equilibria
Depth-Limited Solving for Imperfect-Information Games
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace naïve single-value depth limits with multi-valued state depth-limited solving | Modicum · HUNL vs Baby Tartanian8 | mbb/g: -10 ± 8 -> 6 ± 5 | Helped |
| Replace naïve single-value depth limits with multi-valued state depth-limited solving | Modicum · HUNL vs Slumbot | mbb/g: -1 ± 15 -> 11 ± 9 | Helped |
| Replace rollout value estimates with DNN predictions (ablation) | Modicum · HUNL vs Baby Tartanian8 | mbb/g: 6 ± 5 -> -11 ± 10 | Hurt |
| Offer multiple opponent strategies at the depth limit instead of only the blueprint strategy (ablation) | Depth-limited solving in NLFH · NLFH off-tree 0.75× pot bet | exploitability | Helped |
| Use only one state value at the depth limit (ablation) | Depth-limited solving in NLFH · NLFH off-tree 0.75× pot bet | exploitability | Hurt |
Closest papers by meaning
- Safe and Nested Subgame Solving for Imperfect-Information Games (NeurIPS 2017 · similarity 0.90)
- Opponent-Model Search in Games with Incomplete Information (AAAI 2024 · similarity 0.86)
- Outbidding and Outbluffing Elite Humans: Mastering Liar’s Poker via Self-Play and Reinforcement Learning (NeurIPS 2026 · similarity 0.86)
- Safe and Robust Subgame Exploitation in Imperfect Information Games (ICML 2024 · similarity 0.86)
- State-Constrained Zero-Sum Differential Games with One-Sided Information (ICML 2024 · similarity 0.85)
- The Update-Equivalence Framework for Decision-Time Planning (ICLR 2024 · similarity 0.85)
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