Publications on Policy-space Search Methods
Girgin, S., Ph. Preux
Basis Expansion In Natural Actor Critic Methods
Recent Advances in reinforcement Learning, Springer LNAI 5323
Moriarty, D.E.
, A.C. Schultz, J.J. Grefenstette"Evolutionary Algorithms for Reinforcement Learning"
Journal of Artificial Intelligence Research, Volume 11
(compressed Postscript - 168 KB)
Abstract:
There are two distinct approaches to solving reinforcement learning problems, namely, searching in v...
Reynolds,
Stuart
E-mail: sir@cs.bham.ac.uk
Decision Boundary Partitioning: Variable Resolution Model-Free Reinforcement Learning
ICML-2k
( gzipped Postscript - 241 KB)
Abstract:
This paper presents a method to refine the resolution of a continuous state Q-function. Q-functions ...
Rosenstein,
Michael
, Andrew Barto( mtr@cs.umass.edu)
Robot weightlifting by direct policy search
IJCAI-01
(PDF - 815KB)
Abstract:
This paper describes a method for structuring a robot motor learning task. By
designing a suitably ...
Schmidhuber,
Juergen
E-mail: juergen@idsia.ch
REINFORCEMENT LEARNING AND POMDPs (dozens of papers on RL in partially observable environments since 1989)
Journal papers and conference papers
(HTML - 100KB)
Abstract:
Realistic environments are not fully observable. General learning agents need an internal state to m...
Schmidhuber,
Juergen
E-mail: juergen@idsia.ch
Optimal Ordered Problem Solver
Machine Learning Journal 54, 211-254, 2004.; short version: NIPS 15, 1571-1578, 2003.
(HTML - 200 KB)
Abstract:
OOPS solves one task after another, through search for solution- computing programs. It is an increm...
Strens,
Malcolm
, Andrew Moore( mjstrens@qinetiq.com)
Policy Search using Paired Comparisons
Journal of Machine Learning Research, 3:921-950, 2002.
(pdf - 379)
Abstract:
Direct policy search is a practical way to solve reinforcement learning (RL) problems involving cont...
Strens,
Malcolm
( mjstrens@qinetiq.com)
Efficient hierarchical MCMC for policy search
International Conference on Machine Learning, 2004
(pdf - 318KB)
Abstract:
Many inference and optimization tasks in machine learning can be solved by sampling approaches such ...
Strens,
Malcolm
( mjstrens@qinetiq.com)
Efficient hierarchical MCMC for policy search
International Conference on Machine Learning, 2004
(pdf - 318KB)
Abstract:
Many inference and optimization tasks in machine learning can be solved by sampling approaches such ...
Tresp,
Volker
, Reimar Hofmann( volker.tresp@siemens.com)
Missing and noisy data in nonlinear time-series prediction.
Neural Networks for Signal Processing 5
( gzipped Postscript - )
Abstract:
This paper is now of mostly historical importance. At the time of
publication (1995) it was one of ...