Published June 4, 2022 | Version 1.0
Dataset Open

The MABe22 Benchmarks for Representation Learning of Multi-Agent Behavior

Description

Real-world behavior is often shaped by complex interactions between multiple agents. To scalably study multi-agent behavior, advances in unsupervised and self-supervised learning have enabled many different behavioral representations to be learned from trajectory data. However, such representation learning approaches are generally evaluated on specific datasets and tasks, and it is difficult to compare methods quantitatively to measure progress on representations for behavior analysis. We aim to address this by introducing a large-scale, multi-agent trajectory dataset from real-world behavioral neuroscience experiments that covers a range of behavior analysis tasks. Our dataset consists of common model organisms (mice and flies) in a variety of settings (different strains, lengths of interaction, optogenetic and thermogenetic stimulation), with a subset consisting of expert-annotated behavior labels. Improvements on our dataset corresponds to behavioral representations that work across multiple organisms and is able to capture differences for common behavior analysis tasks. Sample Python notebooks and evaluator to use our dataset is available at: https://www.aicrowd.com/challenges/multi-agent-behavior-challenge-2022

Files

Files (13.4 GB)
Name Size
md5:76a48f3a1679a219a0e7e8a87871cc74
852.7 MB Download
md5:5a54f2d29a13a256aabbefc61a633176
349.9 MB Download
md5:9830cebad167449b5fed658c0f424bd9
5.2 GB Download
md5:9356829421ec71ed7291a222d9c2d031
4.1 GB Download
md5:f14b94f278fb0c1790f9d27256224b2f
987.2 MB Download
md5:20dc132300118a64aac665dd68153b20
1.9 GB Download

Additional details

Created:
September 10, 2022
Modified:
November 18, 2022