  In this paper, we contribute a multi-faceted study into Pavlovian signalling
-- a process by which learned, temporally extended predictions made by one
agent inform decision-making by another agent. Signalling is intimately
connected to time and timing. In service of generating and receiving signals,
humans and other animals are known to represent time, determine time since past
events, predict the time until a future stimulus, and both recognize and
generate patterns that unfold in time. We investigate how different temporal
processes impact coordination and signalling between learning agents by
introducing 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 works to
acquire sparse reward while avoiding time-conditional hazards. We evaluate two
domain variations: machine agents interacting in a seven-state linear walk, and
human-machine interaction 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 between two agents. We further show how to
computationally build this adaptive signalling process out of a fixed
signalling process, characterized by fast continual prediction learning and
minimal constraints on the nature of the agent receiving signals. Our results
therefore suggest an actionable, constructivist path towards communication
learning between reinforcement learning agents.
