Gen Li

Minimax-Optimal Reward-Agnostic Exploration in Reinforcement Learning

This paper studies reward-agnostic exploration in reinforcement learning (RL)—a scenario where the learner is unaware of the reward functions during the exploration stage—and designs an algorithm that improves over the state of the art. More precisely, consider a finite-horizon i...