Dynamic Inverse Reinforcement Learning for Characterizing Animal Behavior
Authors: Zoe Ashwood, Aditi Jha, Jonathan W. Pillow
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 {zashwood, aditijha, pillow}@princeton.edu |
| 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. |