  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. has
no access to rewards -- and an $architect$ which guides the builder towards the
goal of the task. We define and explore a formal setting where artificial
agents are equipped with mechanisms that allow them to simultaneously learn a
task while at the same time evolving a shared communication protocol. Ideally,
such learning should only rely on high-level communication priors and be able
to handle a large variety of tasks and meanings while deriving communication
protocols that can be reused across tasks. We present the Architect-Builder
Problem (ABP): an asymmetrical setting in which an architect must learn to
guide a builder towards constructing a specific structure. The architect knows
the target structure but cannot act in the environment and can only send
arbitrary messages to the builder. The builder on the other hand can act in the
environment, but receives no rewards nor has any knowledge about the task, and
must learn to solve it relying only on the messages sent by the architect.
Crucially, the meaning of messages is initially not defined nor shared between
the agents but must be negotiated throughout learning. Under these constraints,
we propose Architect-Builder Iterated Guiding (ABIG), a solution to ABP where
the architect leverages a learned model of the builder to guide it while the
builder uses self-imitation learning to reinforce its guided behavior. We
analyze the key learning mechanisms of ABIG and test it in 2D tasks involving
grasping cubes, placing them at a given location, or building various shapes.
ABIG results in a low-level, high-frequency, guiding communication protocol
that not only enables an architect-builder pair to solve the task at hand, but
that can also generalize to unseen tasks.

Machine Learning;Artificial Intelligence;Multiagent Systems