We study infinite-horizon average-reward constrained Markov decision processes (CMDPs) under the weakly communicating assumption. Existing high-probability guarantees for this setting either require c...
arXiv:2609.39093v1 Announce Type: new
Abstract: We study infinite-horizon average-reward constrained Markov decision processes (CMDPs) under the weakly communicating assumption. Existing high-probabi...
By Kihyun Yu, Seoungbin Bae, Dabeen Lee
arXiv:2609.36486v1 Announce Type: new
Abstract: We study an unknown-transition finite-horizon Markov decision process (MDP) with a finite collection of known reward functions $\{r^1, r^2, \ldots, r^M...
By Zijun Chen, Zihan Zhang
arXiv:2607. 19854v1 Announce Type: new Abstract: We study horizon-free regret minimization for finite-horizon time-homogeneous tabular Markov decision processes with $S$ states, $A$ actions, horizon $H$, and per-trajectory total reward bounded by $1$.
By Runlong Zhou, Zihan Zhang, Maryam Fazel, Simon S. Du
The paper investigates reinforcement learning with multi‑step transition look‑ahead, where an agent can foresee the states resulting from any sequence of λ actions before choosing its next move. It proves that exact planning remains NP‑hard for every fixed rational discount factor γ in (0,1), and introduces a randomized polynomial‑time approximation scheme that works for any fixed look‑ahead depth. Extending this to unknown transitions and stochastic rewards, the authors develop an algorithm with cumulative regret matching classical tabular discounted RL up to logarithmic factors, showing that efficient near‑optimal planning and learning are achievable despite the NP‑hardness of exact planning.
By Corentin Pla, Hugo Richard, Marc Abeille, Vianney Perchet
arXiv:2510. 06647v2 Announce Type: replace-cross Abstract: We study fine-grained gap-dependent regret bounds for model-free reinforcement learning in episodic tabular Markov Decision Processes.
By Haochen Zhang, Zhong Zheng, Lingzhou Xue