Mean Field for Markov Decision Processes: from Discrete to Continuous Optimization

We study the convergence of Markov decision processes, composed of a large number of objects, to optimization problems on ordinary differential equations. We show that the optimal reward of such a Markov decision process, which satisfies a Bellman equation, converges to the solution of a continuous Hamilton-Jacobi-Bellman (HJB) equation based on the mean field approximation of the Markov decision process. We give bounds on the difference of the rewards and an algorithm for deriving an approximating solution to the Markov decision process from a solution of the HJB equations. We illustrate the method on three examples pertaining, respectively, to investment strategies, population dynamics control and scheduling in queues. They are used to illustrate and justify the construction of the controlled ODE and to show the advantage of solving a continuous HJB equation rather than a large discrete Bellman equation.


Published in:
IEEE Transactions on Automatic Control, 57, 9, 2266-2280
Year:
2012
Publisher:
Institute of Electrical and Electronics Engineers
ISSN:
0018-9286
Keywords:
Laboratories:




 Record created 2012-02-06, last modified 2018-01-28

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