  A growing body of research suggests that embodied gameplay, prevalent not
just in human cultures but across a variety of animal species including turtles
and ravens, is critical in developing the neural flexibility for creative
problem solving, decision making, and socialization. Comparatively little is
known regarding the impact of embodied gameplay upon artificial agents. While
recent work has produced agents proficient in abstract games, these
environments are far removed from the real world and thus these agents can
provide little insight into the advantages of embodied play. Hiding games, such
as hide-and-seek, played universally, provide a rich ground for studying the
impact of embodied gameplay on representation learning in the context of
perspective taking, secret keeping, and false belief understanding. Here we are
the first to show that embodied adversarial reinforcement learning agents
playing Cache, a variant of hide-and-seek, in a high fidelity, interactive,
environment, learn generalizable representations of their observations encoding
information such as object permanence, free space, and containment. Moving
closer to biologically motivated learning strategies, our agents'
representations, enhanced by intentionality and memory, are developed through
interaction and play. These results serve as a model for studying how facets of
vision develop through interaction, provide an experimental framework for
assessing what is learned by artificial agents, and demonstrates the value of
moving from large, static, datasets towards experiential, interactive,
representation learning.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning