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..
State-free Reinforcement Learning
Authors: Mingyu Chen, Aldo Pacchiano, Xuezhou Zhang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we study the state-free RL problem, where the algorithm does not have the states information before interacting with the environment. Specifically, denote the reachable state set by SΠ := {s| maxπ Π q P,π(s) > 0}, we design an algorithm which requires no information on the state space S while having a regret that is completely independent of S and only depend on SΠ. We view this as a concrete first step towards parameter-free RL, with the goal of designing RL algorithms that require no hyper-parameter tuning. |
| Researcher Affiliation | Academia | Mingyu Chen Boston University EMAIL Aldo Pacchiano Boston University Broad Institute of MIT and Harvard EMAIL Xuezhou Zhang Boston University EMAIL |
| Pseudocode | Yes | Algorithm 1 Black-box Reduction for State-free RL (SF-RL) |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | No | This is a theoretical paper focused on algorithm design and regret analysis for MDPs. It does not use real-world datasets for training or experimentation, and therefore no public dataset information is provided. |
| Dataset Splits | No | This is a theoretical paper focused on algorithm design and regret analysis for MDPs. It does not conduct empirical experiments with data, and thus no validation splits are mentioned. |
| Hardware Specification | No | This is a theoretical paper and does not involve empirical experiments. Therefore, no hardware specifications are mentioned for running experiments. |
| Software Dependencies | No | This is a theoretical paper and does not involve empirical experiments. Therefore, no specific software dependencies with version numbers are mentioned for replicating experimental setups. |
| Experiment Setup | No | This is a theoretical paper and does not involve empirical experiments. Therefore, no specific experimental setup details, such as hyperparameters or training settings, are provided. |