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arXiv 2004.134392020-04-28cs.AIcs.LGcs.MA

Improving Sample Efficiency and Multi-Agent Communication in RL-based Train Rescheduling

ZHAW Datalab（苏黎世应用科学大学数据实验室）；ZHAW School of Engineering（苏黎世应用科学大学工程学院）；Swiss Federal Railways（瑞士联邦铁路）

Dano Roost, Ralph Meier, Stephan Huschauer, Erik Nygren, Adrian Egli, Andreas Weiler, Thilo Stadelmann

备注：: Accepted for publication at the 7th Swiss Conference on Data Science (SDS 2020)

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We present preliminary results from our sixth placed entry to the Flatland international competition for train rescheduling, including two improvements for optimized reinforcement learning (RL) training efficiency, and two hypotheses with respect to the prospect of deep RL for complex real-world control tasks: first, that current state of the art policy gradient methods seem inappropriate in the domain of high-consequence environments; second, that learning explicit communication actions (an emerging machine-to-machine language, so to speak) might offer a remedy. These hypotheses need to be confirmed by future work. If confirmed, they hold promises with respect to optimizing highly efficient logistics ecosystems like the Swiss Federal Railways railway network.

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