  Involving humans directly for the benefit of AI agents' training is getting
traction thanks to several advances in reinforcement learning and
human-in-the-loop learning. Humans can provide rewards to the agent,
demonstrate tasks, design a curriculum, or act in the environment, but these
benefits also come with architectural, functional design and engineering
complexities. We present Cogment, a unifying open-source framework that
introduces an actor formalism to support a variety of humans-agents
collaboration typologies and training approaches. It is also scalable out of
the box thanks to a distributed micro service architecture, and offers
solutions to the aforementioned complexities.
