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SUMMARY:Distributionally robust chance-constrained Markov decision process
 es
DTSTART:20241122T080000Z
DTEND:20241122T084000Z
DTSTAMP:20260317T034500Z
UID:indico-event-13449@indico.math.cnrs.fr
DESCRIPTION:Speakers: Immanuel Bomze\n\nMarkov decision process (MDP) is a
  decision making framework where a decision maker is interested in maximiz
 ing the expected discounted value of a stream of rewards received at futur
 e stages at various states which are visited according to a controlled Mar
 kov chain. Many algorithms including linear programming methods are availa
 ble in the literature to compute an optimal policy when the rewards and tr
 ansition probabilities are deterministic. In this talk\, we consider an MD
 P problem where either the reward vector or the transition probability vec
 tor is a random vector. We formulate the MDP problem with distributionally
  robust chance-constrained optimization framework.  As an application\, w
 e study a machine replacement problem and perform numerical experiments on
  randomly generated instances.\n\nhttps://indico.math.cnrs.fr/event/13449/
LOCATION:Salle de conférence (XLIM)
URL:https://indico.math.cnrs.fr/event/13449/
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