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

Some of the most important tasks take place in environments which lack cheapand perfect simulators, thus hampering the application of model-freereinforcement learning (RL). While model-based RL aims to learn a dynamicsmodel, in a more general case the learner does not know a priori what theaction space is. Here we propose a formalism where the learner induces a worldprogram by learning a dynamics model and the actions in graph-basedcompositional environments by observing state-state transition examples. Then,the learner can perform RL with the world program as the simulator for complexplanning tasks. We highlight a recent application, and propose a challenge forthe community to assess world program-based planning.

World Programs for Model-Based Learning and Planning in Compositional State and Action Spaces