RLgraph: Modular Computation Graphs for Deep Reinforcement Learning

Autor:	Schaarschmidt, Michael, Mika, Sven, Fricke, Kai, Yoneki, Eiko
Rok vydání:	2018
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: SysML 2019
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1810.09028 Zobrazit plný text záznamu View this record from Arxiv