Proposal Approximate Dynamic Programming Using Bellman Residual Elimination

The overarching goal of the thesis is to devise new strategies for multi-agent planning and control problems, especially in the case where the agents are subject to random failures, maintenance needs, or other health management concerns, or in cases where the system model is not perfectly known. We argue that dynamic programming techniques, in particular Markov Decision Processes (MDPs), are a natural framework for addressing these planning problems, and present an MDP problem formulation for a persistent surveillance mission that incorporates stochastic fuel usage dynamics and the possibility for randomly-occurring failures into the planning process. We show that this problem formulation and its optimal policy lead to good mission performance in a number of realworld scenarios. Furthermore, an on-line, adaptive solution framework is developed that allows the planning system to improve its performance over time, even in the case where the true system model is uncertain or time-varying. Motivated by the difficulty of solving the persistent mission problem exactly when the number of agents becomes large, we then develop a new family of approximate dynamic programming algorithms, called Bellman Residual Elimination (BRE) methods, which can be employed to approximately solve large-scale MDPs. We analyze these methods and prove a number of desirable theoretical properties about them, including reduction to exact policy iteration under certain conditions. Finally, we apply these BRE methods to large-scale persistent surveillance problems and show that they yield good performance, and furthermore, that they can be successfully integrated into the adaptive planning framework.