  Deep reinforcement learning (DRL) requires large samples and a long training
time to operate optimally. Yet humans rarely require long periods training to
perform well on novel tasks, such as computer games, once they are provided
with an accurate program of instructions. We used perceptual control theory
(PCT) to construct a simple closed-loop model which requires no training
samples and training time within a video game study using the Arcade Learning
Environment (ALE). The model was programmed to parse inputs from the
environment into hierarchically organised perceptual signals, and it computed a
dynamic error signal by subtracting the incoming signal for each perceptual
variable from a reference signal to drive output signals to reduce this error.
We tested the same model across two different Atari paddle games Breakout and
Pong to achieve performance at least as high as DRL paradigms, and close to
good human performance. Our study shows that perceptual control models, based
on simple assumptions, can perform well without learning. We conclude by
specifying a parsimonious role of learning that may be more similar to
psychological functioning.
