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

In hierarchical planning for Markov decision processes (MDPs), temporalabstraction allows planning with macro-actions that take place at differenttime scale in form of sequential composition. In this paper, we propose a novelapproach to compositional reasoning and hierarchical planning for MDPs undertemporal logic constraints. In addition to sequential composition, we introducea composition of policies based on generalized logic composition: Givensub-policies for sub-tasks and a new task expressed as logic compositions ofsubtasks, a semi-optimal policy, which is optimal in planning with onlysub-policies, can be obtained by simply composing sub-polices. Thus, asynthesis algorithm is developed to compute optimal policies efficiently byplanning with primitive actions, policies for sub-tasks, and the compositionsof sub-policies, for maximizing the probability of satisfying temporal logicspecifications. We demonstrate the correctness and efficiency of the proposedmethod in stochastic planning examples with a single agent and multiple taskspecifications.

Compositional planning in Markov decision processes: Temporal abstraction meets generalized logic composition