Decentralized Stochastic Control and Learning
Modern engineering systems are increasingly complex and comprise of many interdependent dynamic parts. This can enable the development of a novel framework to process large amounts of data and deliver real-time control actions that maximize associated benefits. However, as we move to increasingly complex systems, we need to develop new decentralized control approaches to optimize the impact on system behavior of the interaction between its entities.
Centralized stochastic control has been ubiquitous approach to control complex systems with stochastic uncertainties. Centralized stochastic control refers to multi-stage optimization problems of systems with external disturbances and noisy observations by a single decision maker. A key assumption in deriving solutions of centralized stochastic control problems is that the decision maker perfectly recalls all past control actions and observations.
While centralized systems have been extensively studied, their core assumption does not hold for larger systems comprising of multiple agents, e.g., connected automated vehicles, drone swarms, and smart grids. In such systems, all agents simultaneously take a decision at every stage based only on their memory, which comprises of the history of their own observations and local information received through communication with other agents. Finding optimal control strategies for such decentralized systems has been shown to be computationally very hard. Thus, the focus of our research is to develop approaches that can improve the feasibility of deriving optimal control strategies for decentralized systems comprising of multiple agents.
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