An analysis of model-based Interval Estimation for Markov Decision Processes

Journal of Computer and System Sciences ◽

10.1016/j.jcss.2007.08.009 ◽

2008 ◽

Vol 74 (8) ◽

pp. 1309-1331 ◽

Cited By ~ 34

Author(s):

Alexander L. Strehl ◽

Michael L. Littman

Keyword(s):

Markov Decision Processes ◽

Interval Estimation ◽

Decision Processes ◽

Model Based ◽

Markov Decision

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Normative Reasoning with an Adaptive Self-interested Agent Model Based on Markov Decision Processes

Advances in Artificial Intelligence – IBERAMIA 2010 - Lecture Notes in Computer Science ◽

10.1007/978-3-642-16952-6_28 ◽

2010 ◽

pp. 274-283 ◽

Cited By ~ 1

Author(s):

Moser Silva Fagundes ◽

Holger Billhardt ◽

Sascha Ossowski

Keyword(s):

Markov Decision Processes ◽

Decision Processes ◽

Agent Model ◽

Normative Reasoning ◽

Model Based ◽

Markov Decision

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Learning to maximize reward rate: a model based on semi-Markov decision processes

Frontiers in Neuroscience ◽

10.3389/fnins.2014.00101 ◽

2014 ◽

Vol 8 ◽

Cited By ~ 5

Author(s):

Arash Khodadadi ◽

Pegah Fakhari ◽

Jerome R. Busemeyer

Keyword(s):

Markov Decision Processes ◽

Decision Processes ◽

Reward Rate ◽

Model Based ◽

Markov Decision

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An empirical evaluation of interval estimation for Markov decision processes

16th IEEE International Conference on Tools with Artificial Intelligence TAI-04 ◽

10.1109/ictai.2004.28 ◽

2005 ◽

Cited By ~ 8

Author(s):

A.L. Strehl ◽

M.L. Littman

Keyword(s):

Markov Decision Processes ◽

Empirical Evaluation ◽

Interval Estimation ◽

Decision Processes ◽

Markov Decision

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Decision Making Under Uncertainty: A Neural Model Based on Partially Observable Markov Decision Processes

Frontiers in Computational Neuroscience ◽

10.3389/fncom.2010.00146 ◽

2010 ◽

Vol 4 ◽

Cited By ~ 75

Author(s):

Rajesh P. N. Rao

Keyword(s):

Decision Making ◽

Markov Decision Processes ◽

Neural Model ◽

Decision Processes ◽

Decision Making Under Uncertainty ◽

Model Based ◽

Markov Decision ◽

Partially Observable Markov ◽

Partially Observable

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Simple Model-Based Exploration and Exploitation of Markov Decision Processes Using the Elimination Algorithm

MICAI 2007: Advances in Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-540-76631-5_31 ◽

2007 ◽

pp. 327-336

Author(s):

Elizabeth Novoa

Keyword(s):

Simple Model ◽

Markov Decision Processes ◽

Decision Processes ◽

Exploration And Exploitation ◽

Model Based ◽

Elimination Algorithm ◽

Markov Decision

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Learning Control of Dynamical Systems Based on Markov Decision Processes: Research Frontiers and Outlooks

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2012.00673 ◽

2012 ◽

Vol 38 (5) ◽

pp. 673-687 ◽

Cited By ~ 1

Author(s):

Xin XU ◽

Dong SHEN ◽

Yan-Qing GAO ◽

Kai WANG

Keyword(s):

Dynamical Systems ◽

Markov Decision Processes ◽

Learning Control ◽

Decision Processes ◽

Markov Decision ◽

Research Frontiers

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A Framework for Modeling Bounded Rationality: Mis-Specified Bayesian-Markov Decision Processes

SSRN Electronic Journal ◽

10.2139/ssrn.2710475 ◽

2016 ◽

Cited By ~ 1

Author(s):

Ignacio Esponda ◽

Demian Pouzo

Keyword(s):

Bounded Rationality ◽

Markov Decision Processes ◽

Decision Processes ◽

Markov Decision

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A Vector Minimum Superharmonic Approach to Solving Infinite-Horizon Discounted Markov Decision Processes

Journal of the Operational Research Society ◽

10.1038/sj/jors/0431109 ◽

1992 ◽

Vol 43 (11) ◽

pp. 1095-1102

Author(s):

D J White

Keyword(s):

Markov Decision Processes ◽

Infinite Horizon ◽

Decision Processes ◽

Markov Decision

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A Convex Programming Approach for Discrete-Time Markov Decision Processes under the Expected Total Reward Criterion

SIAM Journal on Control and Optimization ◽

10.1137/19m1255811 ◽

2020 ◽

Vol 58 (4) ◽

pp. 2535-2566

Author(s):

François Dufour ◽

Alexandre Genadot

Keyword(s):

Convex Programming ◽

Markov Decision Processes ◽

Discrete Time ◽

Decision Processes ◽

Programming Approach ◽

Total Reward ◽

Markov Decision ◽

Reward Criterion

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Extreme-point solutions in Markov decision processes

Journal of Applied Probability ◽

10.1017/s002190020002413x ◽

1983 ◽

Vol 20 (04) ◽

pp. 835-842

Author(s):

David Assaf

Keyword(s):

Convex Function ◽

Extreme Point ◽

Markov Decision Processes ◽

Convex Functions ◽

Sufficient Conditions ◽

Decision Processes ◽

Markov Decision ◽

Full Solution

The paper presents sufficient conditions for certain functions to be convex. Functions of this type often appear in Markov decision processes, where their maximum is the solution of the problem. Since a convex function takes its maximum at an extreme point, the conditions may greatly simplify a problem. In some cases a full solution may be obtained after the reduction is made. Some illustrative examples are discussed.

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