3 citations · 3 across the 4 of their papers we have counts for
4 papers
Towards Scaling Reinforcement Learning to Massive Populations: Learning Mean-Field Representations
Aditya Makkar, Benjamin Unger, Jeongyeol Kwon +3
Modern multi-agent systems are increasingly deployed at scale over large populations of agents in settings such as ad-auctions, traffic routing, and recommendation systems. The dom…
Video Game Level Design as a Multi-Agent Reinforcement Learning Problem
Sam Earle, Zehua Jiang, Eugene Vinitsky +1
Procedural Content Generation via Reinforcement Learning (PCGRL) offers a method for training controllable level designer agents without the need for human datasets, using metrics…
Decentralized Aerial Manipulation of a Cable-Suspended Load using Multi-Agent Reinforcement Learning
Jack Zeng, Andreu Matoses Gimenez, Eugene Vinitsky +2
This paper presents the first decentralized method to enable real-world 6-DoF manipulation of a cable-suspended load using a team of Micro-Aerial Vehicles (MAVs). Our method levera…
A learning agent that acquires social norms from public sanctions in decentralized multi-agent settings
Eugene Vinitsky, Raphael Köster, John P. Agapiou +3
Society is characterized by the presence of a variety of social norms: collective patterns of sanctioning that can prevent miscoordination and free-riding. Inspired by this, we aim…