Flexible Computation Graphs for Deep Reinforcement Learning

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 high performance RL computation graphs in both static graph and define-by-run paradigms. The resulting implementations yield high performance across different deep learning frameworks and distributed backends.

Citation information

M.Schaarschmi, S. Mika, K. Fricke and E. Yoneki: RLgraph: Flexible Computation Graphs for Deep Reinforcement Learning, SysML, 2019.

Turing affiliated authors