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Created by:

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Last updated:

October 19, 2022

Duration:

Unlimited Duration

FREE

This course includes:

Unlimited Duration

Badge on Completion

Certificate of completion

Unlimited Duration

Description

This course is an introduction to the theory and application of large-scale dynamic programming.

Topics include Markov decision processes, dynamic programming algorithms, simulation-based algorithms, theory and algorithms for value function approximation, and policy search methods. The course examines games and applications in areas such as dynamic resource allocation, finance and queueing networks.

Course Curriculum

  • Markov Decision Processes Unlimited
  • Value Iteration Unlimited
  • Policy Iteration Unlimited
  • Average-Cost Problems Unlimited
  • Average-Cost Problems Computational Methods Unlimited
  • Application of Value Iteration to Optimization of Multiclass Queueing Networks Unlimited
  • Q-Learning Unlimited
  • The ODE Method Unlimited
  • Exploration versus Exploitation Unlimited
  • Introduction to Value Function Approximation Curse of Dimensionality Unlimited
  • Model Selection and Complexity Unlimited
  • Introduction to Value Function Approximation Algorithms Unlimited
  • Temporal-Difference Learning with Value Function Approximation Unlimited
  • Temporal-Difference Learning with Value Function Approximation (cont.) Unlimited
  • Optimal Stopping Problems Unlimited
  • Approximate Linear Programming Unlimited
  • Approximate Linear Programming (cont.) Unlimited
  • Efficient Solutions for Approximate Linear Programming Unlimited
  • Efficient Solutions for Approximate Linear Programming: Factored MDPs Unlimited
  • Policy Search Methods Unlimited
  • Policy Search Methods (cont.) Unlimited

About the instructor

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Instructor Rating

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Courses

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Massachusetts Institute of Technology