  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.
