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..
How RL Agents Behave When Their Actions Are Modified
Authors: Eric D. Langlois, Tom Everitt11586-11594
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we experimentally evaluate the learning algorithms and demonstrate behaviour that is consistent with the theoretical results. |
| Researcher Affiliation | Collaboration | Eric D. Langlois1,2,3, Tom Everitt1 1Deep Mind 2University of Toronto 3Vector Institute EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Q Learning on a MAMDP; Algorithm 2 Virtual Sarsa on a MAMDP; Algorithm 3 Empirical Sarsa on a MAMDP |
| Open Source Code | Yes | Code available at https://github.com/edlanglois/mamdp |
| Open Datasets | No | The paper describes custom environments ("Simulation-Oversight", "Off-Switch", "Whisky-Gold") but does not provide access information (link, DOI, formal citation) for any publicly available datasets used or generated. |
| Dataset Splits | No | The paper mentions training steps and independent runs but does not specify train/validation/test splits or cross-validation setup for any dataset. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory amounts, or detailed computer specifications used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9) needed to replicate the experiments. |
| Experiment Setup | No | The paper describes the environment specifics (e.g., states, rewards) and general training parameters (e.g., "trained to convergence", "10^7 steps") but does not provide concrete hyperparameter values (e.g., learning rate, batch size, optimizer settings) or other specific system-level training configurations in the main text. |