arXiv Statistics ML
On the Hardness of Reinforcement Learning with Transition Look-Ahead
Read the original on arXiv Statistics ML →The Flow has not summarised this story yet — read it at arXiv Statistics ML.
Related stories
arXiv Machine Learning
Sep 11
Near-Optimal Reinforcement Learning with Multi-Step Transition Lookahead
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.
arXiv Machine Learning
Jul 1
End-to-End Efficient RL for Linear Bellman Complete MDPs with Deterministic Transitions
arXiv:2603. 23461v2 Announce Type: replace Abstract: We study reinforcement learning (RL) with linear function approximation in Markov Decision Processes (MDPs) satisfying \emph{linear Bellman completeness} -- a fundamental setting where the Bellman backup of any linear value function remains linear.
arXiv Machine Learning
Oct 15
Average-Reward Policy Identification from a Single Continuing Trajectory
arXiv:2610.20486v1 Announce Type: new Abstract: In reinforcement learning, an agent must explore an unknown environment to learn how to make good decisions. Unlike methods that can freely sample any...
arXiv Statistics ML
Oct 27
Reinforcement Learning with Multi-Step Lookahead Information Via Adaptive Batching
arXiv:2601.10418v2 Announce Type: replace-cross Abstract: We study tabular finite-horizon reinforcement learning problems with multiple steps of lookahead information. Before acting, the learner obse...
arXiv Machine Learning
Jul 7
A Hierarchy of Policy Learning Problems
arXiv:2607. 03385v1 Announce Type: cross Abstract: Policy learning has received substantial attention with the goal of learning policies from observational data for decision-making.
arXiv AI
Jun 2
Reinforcement Learning with Pairwise Preferences in Long-Term Decision Problems
arXiv:2606. 00367v1 Announce Type: cross Abstract: Reinforcement learning problems typically define the goal as maximizing the expected value of a scalar reward function.