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arXiv · 1810.09028

RLgraph: Modular Computation Graphs for Deep Reinforcement Learning

Michael Schaarschmidt·Sven Mika·Kai Fricke·Eiko Yoneki
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Abstract

Reinforcement learning (RL) tasks are challenging to implement, execute and test due to algorithmic instability, hyper-parameter sensitivity, and heterogeneous distributed communication patterns. We argue for the separation of logical component composition, backend graph definition, and distributed execution. To this end, we introduce RLgraph, a library for designing and executing reinforcement learning tasks in both static graph and define-by-run paradigms. The resulting implementations are robust, incrementally testable, and yield high performance across different deep learning frameworks and distributed backends.

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BibTeXRIS

Michael Schaarschmidt, Sven Mika, Kai Fricke, Eiko Yoneki. 2018-10-21. RLgraph: Modular Computation Graphs for Deep Reinforcement Learning. https://arxiv.org/abs/1810.09028

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