Research map / Multi-agent systems and games / Games and equilibria
Large Scale Learning of Agent Rationality in Two-Player Zero-Sum Games
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the first-order primal-dual solver instead of Newton's method | NLQRE forward and backward passes · Randomly generated extensive-form games | runtime | Helped |
| Use the first-order primal-dual solver instead of Newton's method | NLQRE training · One-Card Poker | training time | Helped |
| Use the first-order primal-dual solver instead of sparse linear-system solvers | NLQRE backward pass · Randomly generated extensive-form games | memory usage | Helped |
| Model action probabilities with nested-logit rationality parameters | Nested-logit model · Information gathering dataset | log loss | Helped |
Closest papers by meaning
- Neural Auto-Curricula in Two-Player Zero-Sum Games (NeurIPS 2021 · similarity 0.90)
- Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games (NeurIPS 2023 · similarity 0.90)
- Towards convergence to Nash equilibria in two-team zero-sum games (ICLR 2023 · similarity 0.89)
- Robust and Diverse Multi-Agent Learning via Rational Policy Gradient (NeurIPS 2025 · similarity 0.88)
- Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models (NeurIPS 2024 · similarity 0.88)
- Learning Equilibria in Adversarial Team Markov Games: A Nonconvex-Hidden-Concave Min-Max Optimization Problem (NeurIPS 2024 · similarity 0.87)
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