activity
20192021
most citedFlatland-RL : Multi-Agent Reinforcement Learning on Trains

12 citations · 18 across the 3 of their papers we have counts for

collaborators

5 papers

cs.AI20216 cited

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…

cs.AI202012 cited

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…

math.OC2020

A scalable control design for grid-forming inverters in microgrids

Jeremy Watson, Yemi Ojo, Khaled Laib +1

Microgrids are increasingly recognized as a key technology for the integration of distributed energy resources into the power network, allowing local clusters of load and distribut…

math.OC2020

A Review of Reduced-Order Models for Microgrids: Simplifications vs Accuracy

Yemi Ojo, Jeremy Watson, Ioannis Lestas

Inverter-based microgrids are an important technology for sustainable electrical power systems and typically use droop-controlled grid-forming inverters to interface distributed en…

cs.LG2019

Artificial Intelligence for Prosthetics - challenge solutions

Łukasz Kidziński, Carmichael Ong, Sharada Prasanna Mohanty +47

In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a giv…