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..
Continual Auxiliary Task Learning
Authors: Matthew McLeod, Chunlok Lo, Matthew Schlegel, Andrew Jacobsen, Raksha Kumaraswamy, Martha White, Adam White
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct an in-depth study into the resulting multi-prediction learning system. We conduct the experiment in a TMaze environment... |
| Researcher Affiliation | Academia | Department of Computing Science, University of Alberta EMAIL Martha White, Adam White Department of Computing Science, University of Alberta CIFAR Canada AI Chair, Alberta Machine Intelligence Institute (Amii) |
| Pseudocode | Yes | Algorithm 1 Multi-Prediction Learning System |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes experiments in a TMaze environment and Mountain Car, which are typically environments rather than specific datasets with explicit access information or citations. |
| Dataset Splits | No | The paper describes experiments in reinforcement learning environments but does not specify train/validation/test dataset splits, as it concerns interaction with an environment rather than pre-defined datasets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions algorithms like Tree-Backup and Expected Sarsa, and a stepsize method called Auto, but does not provide specific version numbers for any software dependencies or programming languages. |
| Experiment Setup | Yes | Both use λ = 0.9 and a stepsize method called Auto [Mahmood et al., 2012] designed for online learning. We sweep the initial stepsize and meta stepsizes for Auto. For further details about the agents and optimizer, see Appendix D. |