Research map / Learning theory and algorithms / Submodular and game algorithms
Optimistic Regret Minimization for Extensive-Form Games via Dilated Distance-Generating Functions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use optimistic regret minimization with a dilated Euclidean DGF instead of CFR/CFR+ | Smallmatrix · Smallmatrix | saddle-point gap: up to 12 orders of magnitude better-approximate saddle-points | Helped |
| Use CFR+ | Smallmatrix · Smallmatrix | saddle-point gap convergence rate: $T^{-0.7375}$ | Hurt |
| Use OFTRL instead of CFR+ | Leduc poker · Leduc poker | saddle-point gap | Hurt |
| Use OFTRL instead of CFR+ | Leduc poker · Leduc poker | sum of cumulative regrets | Helped |
| Use optimistic regret minimization instead of CFR+ | Deep game trees · Deep game trees | sum of cumulative regrets | Helped |
| Use optimistic OMD instead of OFTRL | Leduc poker · Leduc poker | sum of cumulative regrets | Hurt |
| Use optimistic OMD with dilated Euclidean DGF | Kuhn poker · Kuhn poker | last-iterate saddle-point gap | Helped |
Closest papers by meaning
- Last-iterate Convergence in Extensive-Form Games (NeurIPS 2021 · similarity 0.94)
- Online Convex Optimization for Sequential Decision Processes and Extensive-Form Games (AAAI 2019 · similarity 0.93)
- Optimism Without Regularization: Constant Regret in Zero-Sum Games (NeurIPS 2025 · similarity 0.91)
- Extensive-Form Game Solving via Blackwell Approachability on Treeplexes (NeurIPS 2024 · similarity 0.91)
- Efficient $\Phi$-Regret Minimization with Low-Degree Swap Deviations in Extensive-Form Games (NeurIPS 2024 · similarity 0.91)
- On the Optimality of Dilated Entropy and Lower Bounds for Online Learning in Extensive-Form Games (NeurIPS 2024 · similarity 0.90)
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