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

Unsupervised representation learning has succeeded with excellent results inmany applications. It is an especially powerful tool to learn a goodrepresentation of environments with partial or noisy observations. In partiallyobservable domains it is important for the representation to encode a beliefstate, a sufficient statistic of the observations seen so far. In this paper,we investigate whether it is possible to learn such a belief representationusing modern neural architectures. Specifically, we focus on one-step frameprediction and two variants of contrastive predictive coding (CPC) as theobjective functions to learn the representations. To evaluate these learnedrepresentations, we test how well they can predict various pieces ofinformation about the underlying state of the environment, e.g., position ofthe agent in a 3D maze. We show that all three methods are able to learn beliefrepresentations of the environment, they encode not only the state information,but also its uncertainty, a crucial aspect of belief states. We also find thatfor CPC multi-step predictions and action-conditioning are critical foraccurate belief representations in visually complex environments. The abilityof neural representations to capture the belief information has the potentialto spur new advances for learning and planning in partially observable domains,where leveraging uncertainty is essential for optimal decision making.

Neural Predictive Belief Representations