Emerson Wenzel — accepted AI-conference papers

2022

ForMIC: Foraging via Multiagent RL With Implicit Communication

Samuel Shaw, Emerson Wenzel, Alexis Walker, Guillaume Sartoretti

RA-L 2022

Multi-agent foraging (MAF) involves distributing a team of agents to search an environment and extract resources from it. Nature provides several examples of highly effective foragers, where individuals within the foraging collective use biological markers (e.g., pheromones) to communicate critical

Cited by 14 Source ↗Scholar ↗

2021

PRIMAL$_2$: Pathfinding Via Reinforcement and Imitation Multi-Agent Learning - Lifelong

Mehul Damani, Zhiyao Luo, Emerson Wenzel, Guillaume Sartoretti

RA-L 2021

Multi-agent path finding (MAPF) is an indispensable component of large-scale robot deployments in numerous domains ranging from airport management to warehouse automation. In particular, this work addresses lifelong MAPF (LMAPF) - an online variant of the problem where agents are immediately assigne

Cited by 189 Source ↗Scholar ↗