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..
Dynamic Inverse Reinforcement Learning for Characterizing Animal Behavior
Authors: Zoe Ashwood, Aditi Jha, Jonathan W. Pillow
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate DIRL in simulated experiments and then apply it to a dataset of mice exploring a labyrinth. |
| Researcher Affiliation | Academia | 1Princeton Neuroscience Institute, Princeton University 2Dept. of Computer Science, Princeton University 3Dept. of Electrical and Computer Engineering, Princeton University EMAIL |
| Pseudocode | Yes | Algorithm 1: DIRL Inference Procedure |
| Open Source Code | Yes | We provide the code as a supplemental material. |
| Open Datasets | Yes | The real data studied in this work is publicly available at https://github.com/markusmeister/Rosenberg-2021-Repository. |
| Dataset Splits | Yes | We then fit the goal maps and time-varying weights to 160 of these trajectories, and held out the remaining 20% of trajectories as a validation set. |
| Hardware Specification | No | Our method is computationally efficient and infers the time-varying reward function from 4000 decisions in the 127-node labyrinth environment in 20 minutes on a laptop. This is not specific enough (e.g., no CPU/GPU model or memory). |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with version numbers in the main text. |
| Experiment Setup | Yes | We, first, initialize the parameters randomly, such that the elements of the goal maps are chosen from U(0, 1) and the time-varying weights are Gaussian distributed. |