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..
Deep Radial-Basis Value Functions for Continuous Control
Authors: Kavosh Asadi, Neev Parikh, Ronald E. Parr, George D. Konidaris, Michael L. Littman6696-6704
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically assess the effectiveness of RBVFs in the context of non-linear regression, valuefunction-only deep RL, and actor-critic deep RL. |
| Researcher Affiliation | Collaboration | 1 Amazon Web Services 2 Brown University 3 Duke University |
| Pseudocode | Yes | Algorithm 1 Pseudo-code for RBF-DQN |
| Open Source Code | Yes | We have released our code: 3.github.com/kavosh8/RBFDQN pytorch |
| Open Datasets | Yes | We evaluate RBF-DQN against state-of-the-art value-function-only deep RL baselines... on 6 Open AI Gym environments. |
| Dataset Splits | No | The paper uses continuous interaction with RL environments (Open AI Gym) rather than fixed static datasets with explicit train/validation/test splits. For the regression task, it mentions sampling 500 inputs for training but no explicit splits. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'pytorch' in the code release link but does not specify a version number for it or any other key software dependencies. |
| Experiment Setup | Yes | Details of our hyper-parameter tuning can be found in the Appendix. |