Research map / Bandits and online learning / Online convex optimization and games
Strategy-Based Warm Starting for Regret Minimization in Games
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| warm start CFR (regrets reset from the average strategy) after 100, 500 or 2500 iterations instead of continuing plain CFR | CFR · Flop Texas Hold'em | exploitability vs iterations: warm started to 97, 490 and 2310 iterations | No clear effect |
| warm start every iteration from the average strategy, storing no regrets | CFR · random 100x100 normal-form games | convergence | No clear effect |
| warm start full-game CFR with an abstract-game MCCFR strategy | CFR · Flop Texas Hold'em | exploitability over time: warm started to T = 70 using lambda = 0.08 | Helped |
| set only the starting strategy without initializing regrets (naive warm start) | regret matching · 2x2 normal-form example | regret: second-iteration regret 1.9933 for Player 1 | Hurt |
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- Accelerating Nash Equilibrium Convergence in Monte Carlo Settings Through Counterfactual Value Based Fictitious Play (NeurIPS 2024 · similarity 0.90)
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