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 work presents a multiscale framework to solve an inverse reinforcementlearning (IRL) problem for continuous-time/state stochastic systems. We takeadvantage of a diffusion wavelet representation of the associated Markov chainto abstract the state space. This not only allows for effectively handling thelarge (and geometrically complex) decision space but also provides moreinterpretable representations of the demonstrated state trajectories and alsoof the resulting policy of IRL. In the proposed framework, the problem isdivided into the global and local IRL, where the global approximation of theoptimal value functions are obtained using coarse features and the localdetails are quantified using fine local features. An illustrative numericalexample on robot path control in a complex environment is presented to verifythe proposed method.

Multiscale Inverse Reinforcement Learning using Diffusion Wavelets