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..
Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning
Authors: Siyuan Zhang, Nan Jiang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform empirical evaluation on Open AI Gym [Bro+16], Atari games [BNVB13], and Mujoco [TET12]. ... For each algorithm, we consider different neural architectures, learning rates, and learning steps as hyperparameters to produce multiple candidate policies (and value functions) for selection; see Table 1 in Appendix C for details. |
| Researcher Affiliation | Academia | Siyuan Zhang Computer Science University of Illinois at Urbana-Champaign EMAIL Nan Jiang Computer Science University of Illinois at Urbana-Champaign EMAIL |
| Pseudocode | Yes | Based on this novel observation, we propose to search for a grid of discretization errors in BVFT and pick the resolution that minimizes the loss (Eq.(2)); see pseudocode in Appendix A. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of their code for the described methodology. |
| Open Datasets | Yes | We use standard of๏ฌine datasets when available (RLUnplugged [Gul+21] for Atari, and D4RL [FKNTL21] for Mu Jo Co)... |
| Dataset Splits | No | The paper mentions re-sampling a subset of the dataset for policy selection (usually of size 50,000) for its evaluation, but does not explicitly describe train/validation/test splits for model training or for the data used in their method's evaluation in a reproducible manner. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x) needed to replicate the experiment. |
| Experiment Setup | Yes | For each algorithm, we consider different neural architectures, learning rates, and learning steps as hyperparameters to produce multiple candidate policies (and value functions) for selection; see Table 1 in Appendix C for details. ... we propose to search for a grid of discretization errors in BVFT and pick the resolution that minimizes the loss (Eq.(2)); see pseudocode in Appendix A. ... Strategy 1 (using BVFT-PE-Q) slightly outperforms Strategy 2, but comes with an additional hyperparameter ฮป; we tuned it on Hopper and use the same constant in all experiments. |