Bayesian inverse reinforcement learning for collective animal movement
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Abstract
Agent-based methods allow for defining simple rules that generate complex group behaviors. The governing rules of such models are typically set a priori, and parameters are tuned from observed behavior trajectories. Instead of making simplifying assumptions across all anticipated scenarios, inverse reinforcement learning provides inference on the short-term (local) rules governing long-term behavior policies by using properties of a Markov decision process. We use the computationally efficient linearly-solvable Markov decision process to learn the local rules governing collective movement for a simulation of the selfpropelled-particle (SPP) model and a data application for a captive guppy population. The estimation of the behavioral decision costs is done in a Bayesian framework with basis function smoothing. We recover the true costs in the SPP simulation and find the guppies value collective movement more than targeted movement toward shelter.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Bayesian inverse reinforcement learning for collective animal movement |
| Series title | Annals of Applied Statistics |
| DOI | 10.1214/21-AOAS1529 |
| Volume | 16 |
| Issue | 2 |
| Year Published | 2022 |
| Language | English |
| Publisher | Project Euclid |
| Contributing office(s) | Coop Res Unit Seattle |
| Description | 15 p. |
| First page | 999 |
| Last page | 1013 |
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