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 consider the sequential decision-making problem of making proactiverequest assignment and rejection decisions for a profit-maximizing operator ofan autonomous mobility on demand system. We formalize this problem as a Markovdecision process and propose a novel combination of multi-agent SoftActor-Critic and weighted bipartite matching to obtain an anticipative controlpolicy. Thereby, we factorize the operator's otherwise intractable actionspace, but still obtain a globally coordinated decision. Experiments based onreal-world taxi data show that our method outperforms state of the artbenchmarks with respect to performance, stability, and computationaltractability.

Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems