Fully Online Decentralized Learning in Stochastic Games with Unknown Independent Chains
The paper develops a fully online, decentralized learning algorithm for multi-agent games where players have unknown local dynamics. The method uses occupancy measures to approximate stationary equilibrium policies without needing transition kernels or player synchronization. This provides a scalable framework for learning strategic policies in large systems based only on local state spaces.