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

In this paper we present an algorithm to compute risk averse policies inMarkov Decision Processes (MDP) when the total cost criterion is used togetherwith the average value at risk (AVaR) metric. Risk averse policies are neededwhen large deviations from the expected behavior may have detrimental effects,and conventional MDP algorithms usually ignore this aspect. We provideconditions for the structure of the underlying MDP ensuring that approximationsfor the exact problem can be derived and solved efficiently. Our findings arenovel inasmuch as average value at risk has not previously been considered inassociation with the total cost criterion. Our method is demonstrated in arapid deployment scenario, whereby a robot is tasked with the objective ofreaching a target location within a temporal deadline where increased speed isassociated with increased probability of failure. We demonstrate that theproposed algorithm not only produces a risk averse policy reducing theprobability of exceeding the expected temporal deadline, but also provides thestatistical distribution of costs, thus offering a valuable analysis tool.

Risk Aversion in Finite Markov Decision Processes Using Total Cost Criteria and Average Value at Risk