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Instructor
Prof. Dimitri Bertsekas
Departments
Electrical Engineering and Computer Science
As Taught In
Fall 2015
Level
Graduate
Topics
Engineering
Computer Science
Theory of Computation
Electrical Engineering
Robotics and Control Systems
Systems Engineering
Systems Optimization
Mathematics
Discrete Mathematics
Probability and Statistics
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6.231 | Fall 2015 | Graduate
Dynamic Programming and Stochastic Control
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Lecture Videos
Approximate Dynamic Programming, Lecture Notes
Monte Carlo Linear Algebra: A Review and Recent Results
Approximate Dynamic Programming, Lecture 1
Approximate Dynamic Programming, Lecture 1, Part 1
Approximate Dynamic Programming, Lecture 1, Part 2
Approximate Dynamic Programming, Lecture 1, Part 3
Approximate Dynamic Programming, Lecture 2
Approximate Dynamic Programming, Lecture 2, Part 1
Approximate Dynamic Programming, Lecture 2, Part 2
Approximate Dynamic Programming, Lecture 2, Part 3
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Assignments
6.231 Homework 8, Fall 2015
6.231 Homework Solution 1, Fall 2015
6.231 Homework Solution 2, Fall 2015
6.231 Homework Solution 3, Fall 2015
6.231 Homework Solution 4, Fall 2015
6.231 Homework Solution 5, Fall 2015
6.231 Homework Solution 6, Fall 2015
6.231 Homework Solution 7, Fall 2015
6.231 Homework Solution 8, Fall 2015
Exams
6.231 Dynamic Programming and Stochastic Control, Fall 2008 Midterm and Solutions
6.231 Dynamic Programming and Stochastic Control, Fall 2009 Midterm
6.231 Dynamic Programming and Stochastic Control, Fall 2009 Solutions
6.231 Dynamic Programming and Stochastic Control, Fall 2011 Midterm and Solutions
6.231 Dynamic Programming and Stochastic Control, Fall 2015 Midterm
6.231 Dynamic Programming and Stochastic Control, Fall 2015 Solutions
Lecture Notes
6.231 Fall 2015 Complete Lecture Notes
6.231 Fall 2015 Lecture 1: Introduction to Dynamic Programming, Examples of Dynamic Programming, Significance of Feedback
6.231 Fall 2015 Lecture 10: Infinite Horizon Problems, Stochastic Shortest Path (SSP) Problems, Bellman’s Equation, Dynamic Programming – Value Iteration, Discounted Problems as a Special Case of SSP
6.231 Fall 2015 Lecture 11: Review of Stochastic Shortest Path Problems, Computation Methods for SSP, Computational Methods for Discounted Problems
6.231 Fall 2015 Lecture 12: Average Cost Per Stage Problems, Connection With Stochastic Shortest Path Problems, Bellman’s Equation, Value Iteration, Policy Iteration
6.231 Fall 2015 Lecture 13: Control of Continuous-Time Markov Chains: Semi-Markov Problems, Problem Formulation: Equivalence to Discrete-Time Problems, Problem Formulation: Equivalence to Discrete-Time Problems, Average Cost Problems
6.231 Fall 2015 Lecture 14: Introduction to Advanced Infinite Horizon Dynamic Programming and Approximation Methods
6.231 Fall 2015 Lecture 15: Review of Basic Theory of Discounted Problems, Monotonicity of Contraction Properties, Contraction Mappings in Dynamic Programming, Discounted Problems: Countable State Space with Unbounded Costs, Generalized Discounted Dynamic Programming, An Introduction to Abstract Dynamic Programming
6.231 Fall 2015 Lecture 16: Review of Computational Theory of Discounted Problems, Value Iteration (VI), Optimistic PI, Computational Methods for Generalized Discounted Dynamic Programming, Asynchronous Algorithms
6.231 Fall 2015 Lecture 17: Undiscounted Problems, Stochastic Shortest Path Problems, Proper and Improper Policies, Analysis and Computational Methods for SSP, Pathologies of SSP, SSP Under Weak Conditions
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Projects
6.231 Fall 2015 Project Topics and References
Course Info
Instructor
Prof. Dimitri Bertsekas
Departments
Electrical Engineering and Computer Science
As Taught In
Fall 2015
Level
Graduate
Topics
Engineering
Computer Science
Theory of Computation
Electrical Engineering
Robotics and Control Systems
Systems Engineering
Systems Optimization
Mathematics
Discrete Mathematics
Probability and Statistics
Learning Resource Types
grading
Exams with Solutions
notes
Lecture Notes
theaters
Other Video
assignment
Problem Sets
Download Course