12 citations · 24 across the 4 of their papers we have counts for
8 papers
Distributed Reinforcement Learning for Robot Teams: A Review
Yutong Wang, Mehul Damani, Pamela Wang +2
Purpose of review: Recent advances in sensing, actuation, and computation have opened the door to multi-robot systems consisting of hundreds/thousands of robots, with promising app…
FCMNet: Full Communication Memory Net for Team-Level Cooperation in Multi-Agent Systems
Yutong Wang, Guillaume Sartoretti
Decentralized cooperation in partially-observable multi-agent systems requires effective communications among agents. To support this effort, this work focuses on the class of prob…
Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World
Florian Laurent, Manuel Schneider, Christian Scheller +24
The Flatland competition aimed at finding novel approaches to solve the vehicle re-scheduling problem (VRSP). The VRSP is concerned with scheduling trips in traffic networks and th…
Flatland-RL : Multi-Agent Reinforcement Learning on Trains
Sharada Mohanty, Erik Nygren, Florian Laurent +11
Efficient automated scheduling of trains remains a major challenge for modern railway systems. The underlying vehicle rescheduling problem (VRSP) has been a major focus of Operatio…
PRIMAL2: Pathfinding via Reinforcement and Imitation Multi-Agent Learning -- Lifelong
Mehul Damani, Zhiyao Luo, Emerson Wenzel +1
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 parti…
Distributed Learning of Decentralized Control Policies for Articulated Mobile Robots
Guillaume Sartoretti, William Paivine, Yunfei Shi +2
State-of-the-art distributed algorithms for reinforcement learning rely on multiple independent agents, which simultaneously learn in parallel environments while asynchronously upd…