PAC RL

Optimistic PAC Reinforcement Learning: the Instance-Dependent View
Optimistic algorithms have been extensively studied for regret minimization in episodic tabular Markov Decision Processes (MDPs), both …
Andrea Tirinzoni, Aymen Al Marjani, Emilie Kaufmann
Cite PDF Arxiv
Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs
In probably approximately correct (PAC) reinforcement learning (RL), an agent is required to identify an $\epsilon$-optimal policy with …
Andrea Tirinzoni, Aymen Al Marjani, Emilie Kaufmann
Cite PDF Slides Arxiv