ProvablyEfficientReinforcementLearningwith LinearFunctionApproximationunderAdaptivity Constraints

Real-world reinforcement learning (RL) applications often come with possibly infinite state and action space, and in such a situation classical RL algorithms developed in the tabular setting are not applicable anymore. A popular approach to overcoming this issue is by applying function approximation techniques to the underlying structures of the Markovdecision processes (MDPs).