Model-Free Reinforcement Learning for Stochastic Games with Linear Temporal Logic Objectives
Alper Kamil Bozkurt Yu Wang Michael M. Zavlanos Miroslav Pajic Alper Kamil Bozkurt Yu Wang Michael M. Zavlanos Miroslav Pajic
We study synthesis of control strategies from linear temporal logic (LTL) objectives in unknown environments. We model this problem as a turn-based zero-sum stochastic game between the controller and the environment, where the transition probabilities and the model topology are fully unknown. The winning condition for the controller in this game is the satisfaction of the given LTL specification, ...