RSS Feed for stochastic control https://solvonauts.org/%3Faction%3Drss_search%26term%3Dstochastic+control RSS Feed for stochastic control 6.231 Dynamic Programming and Stochastic Control (MIT) 6.231 Dynamic Programming and Stochastic Control (MIT) This course covers the basic models and solution techniques for problems of sequential decision making under uncertainty stochastic control Approximation methods for problems involving large state spaces are also presented and discussed This course covers the basic models and solution techniques for problems of sequential decision making under uncertainty stochastic control Approximation methods for problems involving large state spaces are also presented and discussed http://dspace.mit.edu/handle/1721.1/46352 http://dspace.mit.edu/handle/1721.1/46352 6.231 Dynamic Programming and Stochastic Control (MIT) 6.231 Dynamic Programming and Stochastic Control (MIT) 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 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 http://dspace.mit.edu/handle/1721.1/75813 http://dspace.mit.edu/handle/1721.1/75813 6.231 Dynamic Programming and Stochastic Control (MIT) 6.231 Dynamic Programming and Stochastic Control (MIT) The 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 This includes systems with finite or infinite state spaces as well as perfectly or imperfectly observed systems We will also discuss approximation methods for problems involving large state spaces Applications of dynamic programming in a variety of fields will be covered in recitations The 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 This includes systems with finite or infinite state spaces as well as perfectly or imperfectly observed systems We will also discuss approximation methods for problems involving large state spaces Applications of dynamic programming in a variety of fields will be covered in recitations http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-231-dynamic-programming-and-stochastic-control-fall-2011 http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-231-dynamic-programming-and-stochastic-control-fall-2011 6.231 Dynamic Programming and Stochastic Control (MIT) 6.231 Dynamic Programming and Stochastic Control (MIT) Includes audio video content AV special element video The 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 This includes systems with finite or infinite state spaces as well as perfectly or imperfectly observed systems We will also discuss approximation methods for problems involving large state spaces Applications of dynamic programming in a variety of fields will be covered in recitations Includes audio video content AV special element video The 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 This includes systems with finite or infinite state spaces as well as perfectly or imperfectly observed systems We will also discuss approximation methods for problems involving large state spaces Applications of dynamic programming in a variety of fields will be covered in recitations http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-231-dynamic-programming-and-stochastic-control-fall-2015 http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-231-dynamic-programming-and-stochastic-control-fall-2015 6.231 Dynamic Programming and Stochastic Control (MIT) This course covers the basic models and solution techniques for problems of sequential decision making under uncertainty stochastic control Approximation methods for problems involving large state spaces are also presented and discussed https://dspace.mit.edu/handle/1721.1/46352 https://dspace.mit.edu/handle/1721.1/46352 6.231 Dynamic Programming and Stochastic Control (MIT) 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 https://dspace.mit.edu/handle/1721.1/75813 https://dspace.mit.edu/handle/1721.1/75813 6.231 Dynamic Programming and Stochastic Control (MIT) The 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 This includes systems with finite or infinite state spaces as well as perfectly or imperfectly observed systems We will also discuss approximation methods for problems involving large state spaces Applications of dynamic programming in a variety of fields will be covered in recitations https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-231-dynamic-programming-and-stochastic-control-fall-2015 https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-231-dynamic-programming-and-stochastic-control-fall-2015 6.231 Dynamic Programming and Stochastic Control (MIT) The 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 This includes systems with finite or infinite state spaces as well as perfectly or imperfectly observed systems We will also discuss approximation methods for problems involving large state spaces Applications of dynamic programming in a variety of fields will be covered in recitations https://dspace.mit.edu/handle/1721.1/101677 https://dspace.mit.edu/handle/1721.1/101677