English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

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