  Reinforcement learning frameworks have introduced abstractions to implement
and execute algorithms at scale. They assume standardized simulator interfaces
but are not concerned with identifying suitable task representations. We
present Wield, a first-of-its kind system to facilitate task design for
practical reinforcement learning. Through software primitives, Wield enables
practitioners to decouple system-interface and deployment-specific
configuration from state and action design. To guide experimentation, Wield
further introduces a novel task design protocol and classification scheme
centred around staged randomization to incrementally evaluate model
capabilities.
