12 citations · 19 across the 3 of their papers we have counts for
3 papers · 1 filter
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…
Action Space Shaping in Deep Reinforcement Learning
Anssi Kanervisto, Christian Scheller, Ville Hautamäki
Reinforcement learning (RL) has been successful in training agents in various learning environments, including video-games. However, such work modifies and shrinks the action space…
Sample Efficient Reinforcement Learning through Learning from Demonstrations in Minecraft
Christian Scheller, Yanick Schraner, Manfred Vogel
Sample inefficiency of deep reinforcement learning methods is a major obstacle for their use in real-world applications. In this work, we show how human demonstrations can improve…