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

Acquiring driving policies that can transfer to unseen environments ischallenging when driving in dense traffic flows. The design of traffic flow isessential and previous studies are unable to balance interaction andsafety-criticism. To tackle this problem, we propose a socially adversarialtraffic flow. We propose a Contextual Partially-Observable Stochastic Game tomodel traffic flow and assign Social Value Orientation (SVO) as context. Wethen adopt a two-stage framework. In Stage 1, each agent in our socially-awaretraffic flow is driven by a hierarchical policy where upper-level policycommunicates genuine SVOs of all agents, which the lower-level policy takes asinput. In Stage 2, each agent in the socially adversarial traffic flow isdriven by the hierarchical policy where upper-level communicates mistaken SVOs,taken by the lower-level policy trained in Stage 1. Driving policy isadversarially trained through a zero-sum game formulation with upper-levelpolicies, resulting in a policy with enhanced zero-shot transfer capability tounseen traffic flows. Comprehensive experiments on cross-validation verify thesuperior zero-shot transfer performance of our method.

Zero-shot Transfer Learning of Driving Policy via Socially Adversarial Traffic Flow