Friday, January 17, 2020 1:30 pm
-
1:30 pm
EST (GMT -05:00)
Ivana
Kajić,
PhD
candidate
David
R.
Cheriton
School
of
Computer
Science
Emergent communication in multi-agent reinforcement learning has been studied to understand properties of resulting languages, as well as to develop communication systems that are suitable for interaction with humans.
Here, using a cooperative navigation task, we show that agents learn a communication protocol that depends on the features of a gridworld environment they're situated in. We analyze the learned protocol, and demonstrate that it efficiently encodes paths in the environment, while exhibiting basic structure.