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New RL algorithm tackles linear Bellman complete MDPs

By PulseAugur Editorial · [1 sources] ·

A new research paper introduces an efficient reinforcement learning (RL) algorithm for Markov Decision Processes (MDPs) that exhibit linear Bellman completeness and deterministic transitions. This algorithm is designed to be computationally efficient, even for large or infinite action spaces, provided an argmax oracle is available. The proposed method achieves sample and computational complexity that is polynomial in the horizon, feature dimension, and the desired accuracy. AI

IMPACT This research could lead to more efficient AI agents in specific, structured environments.

RANK_REASON The cluster contains a single academic paper on a novel algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New RL algorithm tackles linear Bellman complete MDPs

COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zakaria Mhammedi, Alexander Rakhlin, Nneka Okolo ·

    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 func…

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