PAC learning in turn-based stochastic games with reachability objectives: A decentralized private approach via expected conditional distance

Reachability is the most fundamental logical objective, yet it is notoriously difficult to learn in reinforcement learning settings: even for Markov decision processes, PAC learning of reachability is impossible without additional assumptions. This difficulty also holds in turn-based stochastic game