12 citations · 18 across the 4 of their papers we have counts for
4 papers
Scope Restriction for Scalable Real-Time Railway Rescheduling: An Exploratory Study
Erik Nygren, Christian Eichenberger, Emma Frejinger
With the aim to stimulate future research, we describe an exploratory study of a railway rescheduling problem. A widely used approach in practice and state of the art is to decompo…
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…
Improving Sample Efficiency and Multi-Agent Communication in RL-based Train Rescheduling
Dano Roost, Ralph Meier, Stephan Huschauer +4
We present preliminary results from our sixth placed entry to the Flatland international competition for train rescheduling, including two improvements for optimized reinforcement…