RLgraph: Modular Computation Graphs for Deep Reinforcement Learning

Part of Proceedings of Machine Learning and Systems 1 (MLSys 2019)

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Michael Schaarschmidt, Sven Mika, Kai Fricke, Eiko Yoneki


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.