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On constrained Markov decision processes
Operations Research Letters, 1996zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Evidential Markov Decision Processes
2011This paper proposes a new model, the EMDP (Evidential Markov Decision Process). It is a MDP (Markov Decision Process) for belief functions in which rewards are defined for each state transition, like in a classical MDP, whereas the transitions are modeled as in an EMC (Evidential Markov Chain), i.e.
Hélène Soubaras +2 more
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Competing Markov decision processes
Annals of Operations Research, 1991zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Easy Affine Markov Decision Processes
Operations Research, 2019Individuals, firms, and governments often face the challenge of making optimal decisions in a dynamic setting amidst a changing and uncertain environment. Although Markov decision processes (MDPs) provide a powerful modeling framework for such problems, solving an MDP is generally difficult.
Jie Ning, Matthew J. Sobel
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Variability sensitive Markov decision processes
Proceedings of the 28th IEEE Conference on Decision and Control, 1992Considered are time-average Markov Decision Processes (MDPs) with finite state and action spaces. Two definitions of variability are introduced, namely, the expected time-average variability and time-average expected variability. The two criteria are in general different, although they can both be employed to penalize for variance in the stream of ...
Melike Baykal-Gürsoy, Keith W. Ross
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Coevolutive planning in markov decision processes
Proceedings of the first international joint conference on Autonomous agents and multiagent systems part 2 - AAMAS '02, 2002We investigate the idea of having groups of agents coevolving in order to iteratively refine multi-agent plans. This idea we called coevolution is formalized and analyzed in a general purpose and applied to the stochastic control frameworks that use an explicit model of the world\,: coevolution can directly be adapted to the frameworks of Multi-Agent ...
Scherrer, Bruno, Charpillet, François
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On the significance of Markov decision processes
1997Formulating the problem facing an intelligent agent as a Markov decision process (MDP) is increasingly common in artificial intelligence, reinforcement learning, artificial life, and artificial neural networks. In this short paper we examine some of the reasons for the appeal of this framework.
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Policy Bounds for Markov Decision Processes
Operations Research, 1986This paper demonstrates how a Markov decision process (MDP) can be approximated to generate a policy bound, i.e., a function that bounds the optimal policy from below or from above for all states. We present sufficient conditions for several computationally attractive approximations to generate rigorous policy bounds.
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Accretive Operators and Markov Decision Processes
Mathematics of Operations Research, 1980The dynamic programming functional equation for an abstract, continuous parameter, Markov decision process is shown to involve an operator which is m-accretive, thus giving rise to a nonlinear semigroup, called the Bellman semigroup. A class of controls is specified for which the maximum expected reward over a finite planning horizon is given by this ...
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Markov Decision Process Measurement Model
Psychometrika, 2018Within-task actions can provide additional information on student competencies but are challenging to model. This paper explores the potential of using a cognitive model for decision making, the Markov decision process, to provide a mapping between within-task actions and latent traits of interest. Psychometric properties of the model are explored, and
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