Shayan Karimi

Computational Hardness of Reinforcement Learning with Partial $q^{\pi}$-Realizability

This paper investigates the computational complexity of reinforcement learning within a novel linear function approximation regime, termed partial $q^{\pi}$-realizability. In this framework, the objective is to learn an $\epsilon$-optimal policy with respect to a predefined policy set $\Pi$, under…