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

The principle of maximum entropy is a broadly applicable technique forcomputing a distribution with the least amount of information possible whileconstrained to match empirically estimated feature expectations. However, inmany real-world applications that use noisy sensors computing the featureexpectations may be challenging due to partial observation of the relevantmodel variables. For example, a robot performing apprenticeship learning maylose sight of the agent it is learning from due to environmental occlusion. Weshow that in generalizing the principle of maximum entropy to these types ofscenarios we unavoidably introduce a dependency on the learned model to theempirical feature expectations. We introduce the principle of uncertain maximumentropy and present an expectation-maximization based solution generalized fromthe principle of latent maximum entropy. Finally, we experimentally demonstratethe improved robustness to noisy data offered by our technique in a maximumcausal entropy inverse reinforcement learning domain.

IRL with Partial Observations using the Principle of Uncertain Maximum Entropy