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..
Optimal Exploration for Model-Based RL in Nonlinear Systems
Authors: Andrew Wagenmaker, Guanya Shi, Kevin G. Jamieson
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conclude with experiments demonstrating the effectiveness of our method in realistic nonlinear robotic systems1. |
| Researcher Affiliation | Academia | Andrew Wagenmaker Paul G. Allen School of Computer Science & Engineering University of Washington Seattle, WA 98195 Guanya Shi Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 Kevin Jamieson Paul G. Allen School of Computer Science & Engineering University of Washington Seattle, WA 98195 |
| Pseudocode | Yes | Algorithm 1 Optimal Exploration in Nonlinear Systems (informal) |
| Open Source Code | Yes | Code: https://github.com/ajwagen/nonlinear_sysid_for_control |
| Open Datasets | No | The paper uses simulated systems (drone, car, and a 1-D system example) which are internally generated, not publicly available datasets or benchmarks with specified access information. |
| Dataset Splits | No | The paper conducts experiments on simulated systems using 'episodes' of interaction. It does not provide explicit training, validation, or test dataset splits as one would for a standard machine learning dataset. |
| Hardware Specification | Yes | All experiments were run on a machine with 56 Intel(R) Xeon(R) CPU E5-2690 v4 @ 2.60GHz CPUs, and 64GB RAM. |
| Software Dependencies | No | All code was implemented in Py Torch. However, no specific version numbers for PyTorch or Python are provided. |
| Experiment Setup | Yes | For all examples the noise is distributed as wh N(0, 0.1 I). In all cases we set γ2 = 10H (where γ2 is a bound on Eπexp[PH h=1 u h uh]), and we therefore let Πexp denote the set of all policies satisfying Eπexp[PH h=1 u h uh] γ2. |