StarCraftImage: A Dataset For Prototyping Spatial Reasoning Methods For Multi-Agent Environments

no code implementations • CVPR 2023 • Sean Kulinski, Nicholas R. Waytowich, James Z. Hare, David I. Inouye

Spatial reasoning tasks in multi-agent environments such as event prediction, agent type identification, or missing data imputation are important for multiple applications (e. g., autonomous surveillance over sensor networks and subtasks for reinforcement learning (RL)).

Imputation Reinforcement Learning (RL) +2

A General Framework for Distributed Inference with Uncertain Models

no code implementations • 20 Nov 2020 • James Z. Hare, Cesar A. Uribe, Lance Kaplan, Ali Jadbabaie

Non-Bayesian social learning theory provides a framework that solves this problem in an efficient manner by allowing the agents to sequentially communicate and update their beliefs for each hypothesis over the network.

Learning Theory Two-sample testing

Non-Bayesian Social Learning with Uncertain Models

no code implementations • 9 Sep 2019 • James Z. Hare, Cesar A. Uribe, Lance Kaplan, Ali Jadbabaie

Non-Bayesian social learning theory provides a framework that models distributed inference for a group of agents interacting over a social network.

Learning Theory