Learning Markov Decision Processes under Fully Bandit Feedback

• URL: http://arxiv.org/abs/2602.02260v1
• Date: Mon, 02 Feb 2026 16:03:24 GMT
• Title: Learning Markov Decision Processes under Fully Bandit Feedback
• Authors: Zhengjia Zhuo, Anupam Gupta, Viswanath Nagarajan,
• Abstract summary: A standard assumption in Reinforcement Learning is that the agent observes every visited state-action pair in the associated Decision Process (MDP)<n>We provide the first efficient bandit learning algorithm for episodic MDPs with $widetildeO(sqrtT)$ regret.
• Score: 7.462282493793144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A standard assumption in Reinforcement Learning is that the agent observes every visited state-action pair in the associated Markov Decision Process (MDP), along with the per-step rewards. Strong theoretical results are known in this setting, achieving nearly-tight $Θ(\sqrt{T})$-regret bounds. However, such detailed feedback can be unrealistic, and recent research has investigated more restricted settings such as trajectory feedback, where the agent observes all the visited state-action pairs, but only a single \emph{aggregate} reward. In this paper, we consider a far more restrictive ``fully bandit'' feedback model for episodic MDPs, where the agent does not even observe the visited state-action pairs -- it only learns the aggregate reward. We provide the first efficient bandit learning algorithm for episodic MDPs with $\widetilde{O}(\sqrt{T})$ regret. Our regret has an exponential dependence on the horizon length $\H$, which we show is necessary. We also obtain improved nearly-tight regret bounds for ``ordered'' MDPs; these can be used to model classical stochastic optimization problems such as $k$-item prophet inequality and sequential posted pricing. Finally, we evaluate the empirical performance of our algorithm for the setting of $k$-item prophet inequalities; despite the highly restricted feedback, our algorithm's performance is comparable to that of a state-of-art learning algorithm (UCB-VI) with detailed state-action feedback.

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