Cited By
View all- Neely M(2016)Distributed stochastic optimization via correlated schedulingIEEE/ACM Transactions on Networking10.1109/TNET.2014.238259724:2(759-772)Online publication date: 1-Apr-2016
Distributed optimization in adaptive networks
Pages 887 - 894
Abstract
We develop a protocol for optimizing dynamic behavior of a network of simple electronic components, such as a sensor network, an ad hoc network of mobile devices, or a network of communication switches. This protocol requires only local communication and simple computations which are distributed among devices. The protocol is scalable to large networks. As a motivating example, we discuss a problem involving optimization of power consumption, delay, and buffer overflow in a sensor network.
Our approach builds on policy gradient methods for optimization of Markov decision processes. The protocol can be viewed as an extension of policy gradient methods to a context involving a team of agents optimizing aggregate performance through asynchronous distributed communication and computation. We establish that the dynamics of the protocol approximate the solution to an ordinary differential equation that follows the gradient of the performance objective.
References
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P. L. Bartlett and J. Baxter. Stochastic Optimization of Controlled Markov Decision Processes. In IEEE Conference on Decision and Control, pages 124-129, 2000.
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P. L. Bartlett and J. Baxter. Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. Journal of Computer and System Sciences, 64:133-150, 2002.
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J. Baxter and P. L. Bartlett. Infinite-Horizon Gradient-Based Policy Search. Journal of Artificial Intelligence Research, 15:319-350, 2001.
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J. Baxter, P. L. Bartlett, and L. Weaver. Infinite-Horizon Gradient-Based Policy Search: II. Gradient Ascent Algorithms and Experiments. Journal of Artificial Intelligence Research, 15:351-381, 2001.
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T. Jaakkola, S. P. Singh, and M. I. Jordan. Reinforcement Learning Algorithms for Partially Observable Markov Decision Problems. In Advances in Neural Information Processing Systems 7, pages 345-352, 1995.
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H. J. Kushner and G. Yin. Stochastic Approximation Algorithms and Applications. Springer-Verlag, New York, NY, 1997.
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P. Marbach, O. Mihatsch, and J.N. Tsitsiklis. Call Admission Control and Routing in Integrated Service Networks. In IEEE Conference on Decision and Control, 1998.
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P. Marbach and J.N. Tsitsiklis. Simulation–Based Optimization of Markov Reward Processes. IEEE Transactions on Automatic Control, 46(2):191-209, 2001.
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Index Terms
- Distributed optimization in adaptive networks
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MIT Press
Cambridge, MA, United States
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Published: 09 December 2003
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View all- Neely M(2016)Distributed stochastic optimization via correlated schedulingIEEE/ACM Transactions on Networking10.1109/TNET.2014.238259724:2(759-772)Online publication date: 1-Apr-2016