The Complexity of Decentralized Control of Markov Decision Processes

2002 ◽  
Vol 27 (4) ◽  
pp. 819-840 ◽  
Author(s):  
Daniel S. Bernstein ◽  
Robert Givan ◽  
Neil Immerman ◽  
Shlomo Zilberstein
Keyword(s):  
Markov Decision Processes ◽  
Decentralized Control ◽  
Decision Processes ◽  
Markov Decision

scholarly journals Applications of DEC-MDPs in Multi-Robot Systems

2012 ◽  
pp. 361-384
Author(s):  
Aurélie Beynier ◽  
Abdel-Illah Mouaddib
Keyword(s):  
Markov Decision Processes ◽  
Decentralized Control ◽  
Real World ◽  
Decision Processes ◽  
Decision Problems ◽  
Stochastic Environments ◽  
Robot Systems ◽  
Markov Decision ◽  
Multi Robot

In this chapter, we introduce problematics related to the decentralized control of multi-robot systems. We first describe some applicative domains and review the main characteristics of the decision problems the robots must deal with. Then, we review some existing approaches to solve problems of multiagent decentralized control in stochastic environments. We present the Decentralized Markov Decision Processes and discuss their applicability to real-world multi-robot applications. Then, we introduce OC-DEC-MDPs and 2V-DEC-MDPs which have been developed to increase the applicability of DEC-MDPs.

Extreme-point solutions in Markov decision processes

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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