6.231 Dynamic Programming and Stochastic Control


Class Info

Sequential decision-making via dynamic programming. Unified approach to optimal control of stochastic dynamic systems and Markovian decision problems. Applications in linear-quadratic control, inventory control, resource allocation, scheduling, and planning. Optimal decision making under perfect and imperfect state information. Certainty equivalent, open loop-feedback control, rollout, model predictive control, aggregation, and other suboptimal control methods. Infinite horizon problems: discounted, stochastic shortest path, average cost, and semi-Markov models. Value and policy iteration. Abstract models in dynamic programming. Approximate/neurodynamic programming. Simulation based methods. Discussion of current research on the solution of large-scale problems.

This class has 6.041B, 18.600, 18.100A, 18.100B, and 18.100Q as prerequisites.

6.231 will be offered this semester (Spring 2018). It is instructed by .

This class counts for a total of 12 credits. This is a graduate-level class.

You can find more information at the http://www.google.com/search?&q=MIT+%2B+6.231&btnG=Google+Search&inurl=https site or on the 6.231 Stellar site.

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