Tail Distribution of Regret in Optimistic Reinforcement Learning

We derive instance-dependent tail bounds for the regret of optimism-based reinforcement learning in finite-horizon tabular Markov decision processes with unknown transition dynamics. Focusing on a UCBVI-type algorithm, we characterize the tail distribution of the cumulative regret $R_K$ over $K$ episodes, rather than only its expectation or a single high-probability quantile. We analyze two natural exploration-bonus schedules: (i) a $K$-dependent scheme that explicitly incorporates the total number of episodes $K$, and (ii) a $K$-independent scheme that depends only on the current episode index. For both settings, we obtain an upper bound on $\Pr(R_K \ge x)$ that exhibits a distinctive two-regime structure: a sub-Gaussian tail starting from an instance-dependent scale $m_K$ up to a transition threshold, followed by a sub-Weibull tail beyond that point. We further derive corresponding instance-dependent bounds on the expected regret $\mathbb{E}[R_K]$. The proposed algorithm depends on a tuning parameter $\alpha$, which balances the expected regret and the range over which the regret exhibits a sub-Gaussian tail. To the best of our knowledge, our results provide one of the first comprehensive tail-regret guarantees for a standard optimistic algorithm in episodic reinforcement learning.