  Learned communication between agents is a powerful tool when approaching
decision-making problems that are hard to overcome by any single agent in
isolation. However, continual coordination and communication learning between
machine agents or human-machine partnerships remains a challenging open
problem. As a stepping stone toward solving the continual communication
learning problem, in this paper we contribute a multi-faceted study into what
we term Pavlovian signalling -- a process by which learned, temporally extended
predictions made by one agent inform decision-making by another agent with
different perceptual access to their shared environment. We seek to establish
how different temporal processes and representational choices impact Pavlovian
signalling between learning agents. To do so, we introduce a partially
observable decision-making domain we call the Frost Hollow. In this domain a
prediction learning agent and a reinforcement learning agent are coupled into a
two-part decision-making system that seeks to acquire sparse reward while
avoiding time-conditional hazards. We evaluate two domain variations: 1)
machine prediction and control learning in a linear walk, and 2) a prediction
learning machine interacting with a human participant in a virtual reality
environment. Our results showcase the speed of learning for Pavlovian
signalling, the impact that different temporal representations do (and do not)
have on agent-agent coordination, and how temporal aliasing impacts agent-agent
and human-agent interactions differently. As a main contribution, we establish
Pavlovian signalling as a natural bridge between fixed signalling paradigms and
fully adaptive communication learning. Our results therefore point to an
actionable, constructivist path towards continual communication learning
between reinforcement learning agents, with potential impact in a range of
real-world settings.
