Abstract: We study the problem of fixed-budget pure exploration in reinforcement learning.The goal is to identify a near-optimal policy, given a fixed budget on the number of interactions with the environment.Unlike the standard PAC setting, we do not require the target error level $\epsilon$ and failure rate $\dt$ as input.We propose novel algorithms and provide, to the best of our knowledge, the first instance-dependent theoretical guarantee for this setting. Our analysis yields an $\epsilon$-correctness guarantee with instance-dependent probability, characterizing the budget requirements in terms of the problem-specific hardness of exploration. As a core component of our analysis, we derive an $\epsilon$-good guarantee for the multiple bandit problem—solving multiple multi-armed bandit instances simultaneously—which may be of independent interest. To enable our analysis, we also develop tools for reward-free exploration under the fixed-budget setting, which we believe will be useful for future work in this area.