Wield: Systematic Reinforcement Learning With Progressive Randomization

Michael Schaarschmidt; Kai Fricke; Eiko Yoneki

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arXiv · 1909.06844

Wield: Systematic Reinforcement Learning With Progressive Randomization

Michael Schaarschmidt·Kai Fricke·Eiko Yoneki

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Abstract

Reinforcement learning frameworks have introduced abstractions to implement and execute algorithms at scale. They assume standardized simulator interfaces but are not concerned with identifying suitable task representations. We present Wield, a first-of-its kind system to facilitate task design for practical reinforcement learning. Through software primitives, Wield enables practitioners to decouple system-interface and deployment-specific configuration from state and action design. To guide experimentation, Wield further introduces a novel task design protocol and classification scheme centred around staged randomization to incrementally evaluate model capabilities.

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Michael Schaarschmidt, Kai Fricke, Eiko Yoneki. 2019-09-15. Wield: Systematic Reinforcement Learning With Progressive Randomization. https://arxiv.org/abs/1909.06844

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