11.
B. Bakker and J. Schmidhuber.
Hierarchical Reinforcement
Learning Based on Subgoal Discovery and Subpolicy Specialization
(PDF).
In F. Groen, N. Amato, A. Bonarini, E. Yoshida, and B. Kröse (Eds.),
Proceedings of the 8-th Conference on Intelligent Autonomous Systems,
IAS-8, Amsterdam, The Netherlands, p. 438-445, 2004.
10.
B. Bakker and J. Schmidhuber.
Hierarchical Reinforcement Learning with Subpolicies Specializing
for Learned Subgoals (PDF).
In M. H. Hamza (Ed.), Proceedings of the 2nd IASTED International
Conference on Neural Networks and Computational Intelligence,
NCI 2004, Grindelwald, Switzerland, p. 125-130, 2004.
9. An optimal way of creating and solving subgoals in general
reinforcement learning settings is the
Goedel machine
(J. Schmidhuber, 2003).
8. A bias-optimal way of creating and solving subgoals in the
context of ordered problem sequences is the
Optimal Ordered Problem Solver
(J. Schmidhuber, 2002-2004).
7.
R. Salustowicz and J. Schmidhuber.
Learning to predict through PIPE and automatic task decomposition.
Technical Report IDSIA-11-98, IDSIA, April 1998.
6.
M. Wiering and J. Schmidhuber.
HQ-Learning.
Adaptive Behavior 6(2):219-246, 1997 (122 K).
PDF
.
HTML.
5.
M. Wiering and J. Schmidhuber.
HQ-Learning: Discovering Markovian subgoals for non-Markovian
reinforcement learning.
Technical Report IDSIA-95-96, IDSIA, October 1996.
4.
J. Schmidhuber.
Netzwerkarchitekturen, Zielfunktionen und Kettenregel.
(Net architectures, objective functions, and chain rule.)
Habilitation (postdoctoral thesis - qualification for a
tenure professorship),
Institut für Informatik, Technische Universität
München, 1993 (496 K).
PDF .
HTML.
3.
J. Schmidhuber and R. Wahnsiedler.
Planning simple trajectories using neural subgoal generators.
In J. A. Meyer, H. L. Roitblat, and S. W. Wilson, editors, Proc.
of the 2nd International Conference on Simulation of Adaptive Behavior,
pages 196-202. MIT Press, 1992.
PDF .
HTML without images.
HTML & images in German.
2.
J. Schmidhuber.
Learning to generate sub-goals for action sequences.
In T. Kohonen,
K. Mäkisara, O. Simula, and J. Kangas, editors,
Artificial Neural Networks, pages 967-972. Elsevier Science Publishers
B.V., North-Holland, 1991.
PDF .
HTML.
HTML & images in German.
1.
J. Schmidhuber.
Towards compositional learning with dynamic neural networks.
Technical Report FKI-129-90, Institut für Informatik, Technische
Universität München, 1990.
