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/

Zendy Plus

Presents the zaia usage as premium feature

ZAIA

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the access of premium content as premium feature

Premium Content

Zendy Tools

Zendy Open

In this paper, we consider a traffic game where many atomic agents try to optimize their utilities by choosing the route with the least travel cost, and propose an actor-critic-based adaptive learning algorithm that converges to ɛ-Nash equilibrium with high probability in traffic games. The model consists of an N-person repeated game where each player knows his action space and the realized payoffs he has experienced but is unaware of the information about the action(s) he did not select. We formulate this traffic game as a stochastic congestion game and propose a naive user algorithm for finding a pure Nash equilibrium. An analysis of the convergence is based on Markov chain. Finally, using a single origin-destination network connected by some overlapping paths, the validity of the proposed algorithm is tested

Adaptive Learning Algorithms for Traffic Games with Naive Users