Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations
Authors: Ayush Sekhari, Christoph Dann, Mehryar Mohri, Yishay Mansour, Karthik Sridharan
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide an algorithm for this setting whose error is bounded in terms of the rank d of the underlying MDP. Specifically, our algorithm enjoys a sample complexity bound of e O (H4d K3d log | |)/"2# where H is the length of episodes, K is the number of actions and " > 0 is the desired sub-optimality. We also provide a nearly matching lower bound for this agnostic setting that shows that the exponential dependence on rank is unavoidable, without further assumptions. |
| Researcher Affiliation | Collaboration | Christoph Dann Google Research EMAIL Yishay Mansour Google Research & Tel Aviv University EMAIL Mehryar Mohri Google & Courant Institute EMAIL Ayush Sekhari Cornell University EMAIL Karthik Sridharan Cornell University EMAIL |
| Pseudocode | Yes | Algorithm 1 Policy search algorithm Input: horizon H, rank d, number of episodes n, finite policy class [...] Algorithm 2 Value estimation by autoregressive extrapolation |
| Open Source Code | No | The paper does not contain any statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper describes a theoretical framework and algorithms for Reinforcement Learning but does not refer to or provide access information for any specific publicly available or open datasets for training. |
| Dataset Splits | No | This is a theoretical paper and does not describe empirical experiments that would involve training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental setup or hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details such as hyperparameter values or training configurations. |