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 [1].
Disentangling Reafferent Effects by Doing Nothing
Authors: Benedict Wilkins, Kostas Stathis
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental Evaluation Alg. 1 is applied to three different environments. To demonstrate the effectiveness and general applicability of our approach, we perform a number of experiments. Each showcases, or has a parallel with, an important concept or experiment performed in related fields. |
| Researcher Affiliation | Academia | Benedict Wilkins, Kostas Stathis Department of Computer Science, Royal Holloway University of London EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Estimating Effects via SGD |
| Open Source Code | Yes | All code and data is publicly available1. 1https://github.com/Benedict Wilkins/disentangling-reafference |
| Open Datasets | Yes | Alg. 1 is applied to three different environments. ... (i) Cartpole ... (ii) Atari Freeway ... (iii) Artificial Ape ... (Wilkins and Stathis 2022). |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits needed to reproduce the experiment. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used to run its experiments. |
| Software Dependencies | No | The paper mentions software components like 'neural network' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | Training and model details for each experiment, as well as additional experiments are presented in the supplementary material. The main text does not provide specific hyperparameters or system-level training settings. |