High-Probability Nash Regret for Decentralized Learning in Markov alpha-Potential Games: Episodic and Fully Online Asynchronous Algorithms with Applications to Markov Congestion Games
cs.LG, cs.GT, cs.MA, cs.SY, eess.SY, math.OC
Submitted: 2026-09-14
Updated: 2026-09-14
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Scalable and Independent Learning of Nash Equilibrium Policies in $n$-Player Stochastic Games with Unknown Independent Chains
- Learning Distributed Equilibria in Linear-Quadratic Stochastic Differential Games: An $\alpha$-Potential Approach
- Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics
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