Results 81 to 90 of about 7,880,220 (196)

Robust Markov Decision Processes [PDF]

Markov decision processes (MDPs) are powerful tools for decision making in uncertain dynamic environments. However, the solutions of MDPs are of limited practical use due to their sensitivity to distributional model parameters, which are typically ...
Berç Rustem, Wolfram Wiesemann, Daniel Kuhn   +2 more
core  

The value functions of Markov decision processes

Operations Research Letters, 2016
We provide a full characterization of the set of value functions of Markov decision processes.
Ehud Lehrer, Eilon Solan, Omri N. Solan
openaire   +2 more sources

Portfolio allocation under the vendor managed inventory: A Markov decision process

Journal of Applied Sciences and Environmental Management, 2017
Markov decision processes have been applied in solving a wide range of optimization problems over the years. This study provides a review of Markov decision processes and investigates its suitability for solutions to portfolio allocation problems under ...
VO Ezugwu, LI Igbinosun
doaj   +1 more source

A Deep Hierarchical Reinforcement Learning Algorithm in Partially Observable Markov Decision Processes

IEEE Access, 2018
In recent years, reinforcement learning (RL) has achieved remarkable success due to the growing adoption of deep learning techniques and the rapid growth of computing power.
Tuyen P. Le, Ngo Anh Vien, TaeChoong Chung   +2 more
doaj   +1 more source

Creativity and Markov Decision Processes

CoRR
Creativity is already regularly attributed to AI systems outside specialised computational creativity (CC) communities. However, the evaluation of creativity in AI at large typically lacks grounding in creativity theory, which can promote inappropriate attributions and limit the analysis of creative behaviour.
Guckelsberger Christian, Ady Nadia M., Lahikainen Joonas   +2 more
openaire   +6 more sources

Identification of optimal policies in Markov decision processes [PDF]

, 2010
summary:In this note we focus attention on identifying optimal policies and on elimination suboptimal policies minimizing optimality criteria in discrete-time Markov decision processes with finite state space and compact action set.
Sladký, Karel
core   +1 more source

Nonparametric Identification of Behavioral Responses to Counterfactual Policy Interventions in Dynamic Discrete Decision Processes [PDF]

This paper deals with identification in Markov dynamic discrete decision processes. It shows the nonparametric identification of the behavioral responses to counterfactual policy interventions that modify the one- period utility function.Dynamic discrete
Victor Aguirregabiria
core  

Nonlinearly Perturbed Birth-Death-Type Semi-Markov Processes

, 2017
In Chapter 5, we present asymptotic expansions for stationary and conditional quasi-stationary distributions of nonlinearly perturbed birth-death-type semi-Markov processes, which play an important role in many applications.
Sergei Silvestrov, Silvestrov, Sergei,, Silvestrov, D.,, Dmitrii Silvestrov   +3 more
core   +1 more source

Undiscounted Semi-Markov Decision Processes with Countably Infinite Action Spaces

Algorithms
In this article, we study semi-Markov decision processes (SMDPs) under the limiting ratio average (undiscounted) pay-off criterion, where the state space is finite and the action space of the decision maker is possibly countably infinite.
Kushal Guha Bakshi, Sagnik Sinha, Ramakant Bhardwaj, Purvee Bhardwaj, Satyendra Narayan   +4 more
doaj   +1 more source

A Version of the Euler Equation in Discounted Markov Decision Processes

Journal of Applied Mathematics, 2012
This paper deals with Markov decision processes (MDPs) on Euclidean spaces with an infinite horizon. An approach to study this kind of MDPs is using the dynamic programming technique (DP).
H. Cruz-Suárez, G. Zacarías-Espinoza, V. Vázquez-Guevara   +2 more
doaj   +1 more source