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

Direct policy search (DPS) and look-ahead tree (LT) policies are two widelyused classes of techniques to produce high performance policies for sequentialdecision-making problems. To make DPS approaches work well, one crucial issueis to select an appropriate space of parameterized policies with respect to thetargeted problem. A fundamental issue in LT approaches is that, to take gooddecisions, such policies must develop very large look-ahead trees which mayrequire excessive online computational resources. In this paper, we propose anew hybrid policy learning scheme that lies at the intersection of DPS and LT,in which the policy is an algorithm that develops a small look-ahead tree in adirected way, guided by a node scoring function that is learned through DPS.The LT-based representation is shown to be a versatile way of representingpolicies in a DPS scheme, while at the same time, DPS enables to significantlyreduce the size of the look-ahead trees that are required to take high-qualitydecisions.  We experimentally compare our method with two other state-of-the-art DPStechniques and four common LT policies on four benchmark domains and show thatit combines the advantages of the two techniques from which it originates. Inparticular, we show that our method: (1) produces overall better performingpolicies than both pure DPS and pure LT policies, (2) requires a substantiallysmaller number of policy evaluations than other DPS techniques, (3) is easy totune and (4) results in policies that are quite robust with respect toperturbations of the initial conditions.

Optimized Look-Ahead Tree Policies: A Bridge Between Look-Ahead Tree Policies and Direct Policy Search