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

We study the minmax optimization problem introduced in [22] for computingpolicies for batch mode reinforcement learning in a deterministic setting.First, we show that this problem is NP-hard. In the two-stage case, we providetwo relaxation schemes. The first relaxation scheme works by dropping someconstraints in order to obtain a problem that is solvable in polynomial time.The second relaxation scheme, based on a Lagrangian relaxation where allconstraints are dualized, leads to a conic quadratic programming problem. Wealso theoretically prove and empirically illustrate that both relaxationschemes provide better results than those given in [22].

Min Max Generalization for Two-stage Deterministic Batch Mode Reinforcement Learning: Relaxation Schemes