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 | Conference PDF | Archive PDF | Plain Text | 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. |