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Decision Making in Large Scale Systems >> Content Detail



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LEC #TOPICSKEY DATES
1Markov Decision Processes

Finite-Horizon Problems: Backwards Induction

Discounted-Cost Problems: Cost-to-Go Function, Bellman's Equation
2Value Iteration

Existence and Uniqueness of Bellman's Equation Solution

Gauss-Seidel Value Iteration
3Optimality of Policies derived from the Cost-to-go Function

Policy Iteration

Asynchronous Policy Iteration
Problem set 1 out
4Average-Cost Problems

Relationship with Discounted-Cost Problems

Bellman's Equation

Blackwell Optimality
Problem set 1 due
5Average-Cost Problems

Computational Methods
6Application of Value Iteration to Optimization of Multiclass Queueing Networks

Introduction to Simulation-based Methods Real-Time Value Iteration
Problem set 2 out
7Q-Learning

Stochastic Approximations
8Stochastic Approximations: Lyapunov Function Analysis

The ODE Method

Convergence of Q-Learning
9Exploration versus Exploitation: The Complexity of Reinforcement Learning
10Introduction to Value Function Approximation

Curse of Dimensionality

Approximation Architectures
11Model Selection and ComplexityProblem set 3 out
12Introduction to Value Function Approximation Algorithms

Performance Bounds
13Temporal-Difference Learning with Value Function Approximation
14Temporal-Difference Learning with Value Function Approximation (cont.)
15Temporal-Difference Learning with Value Function Approximation (cont.)

Optimal Stopping Problems

General Control Problems
16Approximate Linear ProgrammingProblem set 4 out
17Approximate Linear Programming (cont.)
18Efficient Solutions for Approximate Linear Programming
19Efficient Solutions for Approximate Linear Programming: Factored MDPs
20Policy Search MethodsProblem set 5 out
21Policy Search Methods (cont.)
22Policy Search Methods for POMDPs

Application: Call Admission Control

Actor-Critic Methods
23Guest Lecture: Prof. Nick Roy

Approximate POMDP Compression
24Policy Search Methods: PEGASUS

Application: Helicopter Control

 








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