Tighter Regret Bounds for Contextual Action-Set Reinforcement Learning
We study episodic reinforcement learning with fixed reward and transition functions, but with episode-dependent admissible action sets that are observed at the start of each episode. Performance is measured by cumulative regret against the episode-wise optimal value, $\sum_{k=1}^K [V^{*,M^k} - V^{π^k,M^k}]$, where $M^k$ represents the action context in the $k$-th episode. We show that the MVP algorithm naturally extends to this framework and enjoys strong theoretical guarantees. In particular, we establish a minimax regret bound of $\widetilde{O}(\sqrt{SAH^3K\log L})$ for adversarial contexts, where $L$ denotes the number of possible contexts. This result implies a regret bound of $\widet...
| Authors | Zijun Chen, Zihan Zhang |
|---|---|
| Primary category | cs.LG |
| Published | 15 May 2026 |
| Last updated at source | 30 September 2026 |
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