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We are interested in interactive agents that learn to coordinate, namely, a$builder$ -- which performs actions but ignores the goal of the task, i.e. hasno access to rewards -- and an $architect$ which guides the builder towards thegoal of the task. We define and explore a formal setting where artificialagents are equipped with mechanisms that allow them to simultaneously learn atask while at the same time evolving a shared communication protocol. Ideally,such learning should only rely on high-level communication priors and be ableto handle a large variety of tasks and meanings while deriving communicationprotocols that can be reused across tasks. We present the Architect-BuilderProblem (ABP): an asymmetrical setting in which an architect must learn toguide a builder towards constructing a specific structure. The architect knowsthe target structure but cannot act in the environment and can only sendarbitrary messages to the builder. The builder on the other hand can act in theenvironment, but receives no rewards nor has any knowledge about the task, andmust learn to solve it relying only on the messages sent by the architect.Crucially, the meaning of messages is initially not defined nor shared betweenthe agents but must be negotiated throughout learning. Under these constraints,we propose Architect-Builder Iterated Guiding (ABIG), a solution to ABP wherethe architect leverages a learned model of the builder to guide it while thebuilder uses self-imitation learning to reinforce its guided behavior. Weanalyze the key learning mechanisms of ABIG and test it in 2D tasks involvinggrasping cubes, placing them at a given location, or building various shapes.ABIG results in a low-level, high-frequency, guiding communication protocolthat not only enables an architect-builder pair to solve the task at hand, butthat can also generalize to unseen tasks.

Learning to Guide and to Be Guided in the Architect-Builder Problem