Polylogarithmic Nash Regret in Matrix Games with Bandit Feedback
cs.LG, cs.GT
Submitted: 2026-09-28
Updated: 2026-09-28
Terminology
Sources
- Decentralized Best-Response-Based Learning in Two-Player Zero-Sum Stochastic Games: A Finite-Sample Analysis
- Finite-Sample Guarantees for Learning Dynamics in Zero-Sum Polymatrix Games
- Optimal last-iterate convergence in matrix games with bandit feedback using the log-barrier
- Near-Optimal Last-Iterate Convergence for Zero-Sum Games with Bandit Feedback and Opponent Actions
- Adversarial Learning in Games with Bandit Feedback: Logarithmic Pure-Strategy Maximin Regret
- On the Limitations and Possibilities of Nash Regret Minimization in Zero-Sum Matrix Games under Noisy Feedback
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks