6.231 Dynamic Programming and Stochastic Control, Fall 2008

Bertsekas, Dimitri

Author(s)

Bertsekas, Dimitri

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Alternative title

Dynamic Programming and Stochastic Control

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Abstract

This course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). We will consider optimal control of a dynamical system over both a finite and an infinite number of stages (finite and infinite horizon). We will also discuss some approximation methods for problems involving large state spaces. Applications of dynamic programming in a variety of fields will be covered in recitations.

Date issued

2008-12

URI

http://hdl.handle.net/1721.1/75813

Department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Other identifiers

6.231-Fall2008

local: 6.231

local: IMSCP-MD5-5c25e9035021832542e5f35f56b312cc

Keywords

dynamic programming, stochastic control, decision making, uncertainty, sequential decision making, finite horizon, infinite horizon, approximation methods, state space, large state space, optimal control, dynamical system, dynamic programming and optimal control, deterministic systems, shortest path, state information, rollout, stochastic shortest path, approximate dynamic programming

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