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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Deep reinforcement learning (DRL) has proven to be an effective tool forcreating general video-game AI. However most current DRL video-game agentslearn end-to-end from the video-output of the game, which is superfluous formany applications and creates a number of additional problems. Moreimportantly, directly working on pixel-based raw video data is substantiallydistinct from what a human player does.In this paper, we present a novel methodwhich enables DRL agents to learn directly from object information. This isobtained via use of an object embedding network (OEN) that compresses a set ofobject feature vectors of different lengths into a single fixed-length unifiedfeature vector representing the current game-state and fulfills the DRLsimultaneously. We evaluate our OEN-based DRL agent by comparing to severalstate-of-the-art approaches on a selection of games from the GVG-AICompetition. Experimental results suggest that our object-based DRL agentyields performance comparable to that of those approaches used in ourcomparative study.

Learning to Play General Video-Games via an Object Embedding Network