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

This paper investigates the effect of learning a forward model on theperformance of a statistical forward planning agent. We transform Conway's Gameof Life simulation into a single-player game where the objective can be eitherto preserve as much life as possible or to extinguish all life as quickly aspossible.  In order to learn the forward model of the game, we formulate the problem ina novel way that learns the local cell transition function by creating a set ofsupervised training data and predicting the next state of each cell in the gridbased on its current state and immediate neighbours. Using this method we areable to harvest sufficient data to learn perfect forward models by observingonly a few complete state transitions, using either a look-up table, a decisiontree or a neural network.  In contrast, learning the complete state transition function is a much hardertask and our initial efforts to do this using deep convolutional auto-encoderswere less successful.  We also investigate the effects of imperfect learned models on predictionerrors and game-playing performance, and show that even models with significanterrors can provide good performance.

A Local Approach to Forward Model Learning: Results on the Game of Life Game