High Performance Across Two Atari Paddle Games Using the Same Perceptual Control Architecture Without Training

TL;DR

This paper demonstrates that a simple perceptual control model can perform well on Atari paddle games without any training, achieving results comparable to deep reinforcement learning and human performance.

Contribution

The study introduces a non-learning, perceptual control-based model that effectively operates across different Atari games without training, challenging the necessity of learning in such tasks.

Findings

Model achieved performance comparable to DRL methods.

Model performed close to human levels on Atari games.

No training samples or time were required for the model.

Abstract

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.