Search Results for author: Ralph Meier

Found 1 papers, 0 papers with code

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

no code implementations28 Apr 2020 Dano Roost, Ralph Meier, Stephan Huschauer, Erik Nygren, Adrian Egli, Andreas Weiler, Thilo Stadelmann

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.

Policy Gradient Methods reinforcement-learning +1

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