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A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes
Published on 2013-01-162831 Views
Parametric policy search algorithms are one of the methods of choice for the optimisation of Markov Decision Processes, with Expectation Maximisation and natural gradient ascent being considered the c
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Presentation
A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes00:00
Outline00:04
Introduction00:06
Markov Decision Processes - Examples00:07
Notation00:46
MDP Solution Methods (1)02:47
MDP Solution Methods (2)03:09
MDP Solution Methods (3)03:16
MDP Solution Methods (4)03:18
MDP Solution Methods (5)03:20
MDP Solution Methods (6)03:24
Parametric Policy Search Methods03:36
Steepest Gradient Ascent (1)04:08
Steepest Gradient Ascent (2)04:48
Expectation Maximisation05:02
Contributions (1)05:43
Contributions (2)06:31
Contributions (3)06:57
Contributions (4)07:03
Analysis of Parametric Policy Search Methods07:22
Analysis - Overview (1)07:24
Analysis - Overview (2)07:31
Natural Gradient Ascent - Analysis08:05
Expectation Maximisation - Analysis08:47
Analysis Summary09:34
Approximate Newton Methods10:02
Approximate Newton Methods (1)10:10
Approximate Newton Methods - Properties (1)10:37
niApproximate Newton Methods - Properties (2)11:21
Approximate Newton Methods - Properties (3)12:07
Experiments12:33
Tetris Experiment12:33
Tetris - Experiment 112:43
Tetris - Experiment 213:19
Linear System Experiment (1)13:46
Linear System Experiment (2)13:49
Non-Linear System Experiment (1)14:15
Summary & Future Work14:53
Summary14:56
Future Work15:05
Thank You!15:06