Reinforcement learning for MDPs with constraints

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4212 LNAI, Issue , 2006, Pages 646-653

  Geibel, Peter ^a  

^a UNIVERSITY OF OSNABRÜCK   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CONSTRAINT THEORY; DECISION THEORY; MARKOV PROCESSES; PROBABILITY; PROBLEM SOLVING;

INEQUALITY CONSTRAINTS; MARKOV DECISION PROCESSES (MDP); REINFORCEMENT LEARNING;

LEARNING SYSTEMS;

EID: 33750372439     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11871842_63     Document Type: Conference Paper

References (8)

1. E. Altman. Constrained Markov Decision Processes. Chapman and Hall/CRC, 1999.
2. D. P. Bertsekas. Dynamic Programming and Optimal Control. Athena Scientific, Belmont, Massachusetts, 1995. Volumes 1 and 2.
3. D. A. Dolgov and E. H. Durfee. Constructing optimal policies for agents with constrained architectures. In AAMAS, pages 974-975, 2003.
4. D.A. Dolgov and E.H. Durfee. Approximating optimal policies for agents with limited execution resources. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, pages 1107-1112. AAAI Press, 2004.
5. E.A. Feinberg and A. Shwartz. Constrained markov decision models with weighted discounted rewards. Math. of Operations Research, 20(4):302-320, 1995.
6. Zoltan Gabor, Zsolt Kalmar, and Csaba Szepesvari. Multi-criteria reinforcement learning. In Proc. 15th International Conf. on Machine Learning, pages 197-205. Morgan Kaufmann, San Francisco, CA, 1998.
7. P. Geibel and F. Wysotzki. Risk-sensitive reinforcement learning applied to chance constrained control. JAIR, 24:81-108, 2005.
8. R. S. Sutton and A. G. Barto. Reinforcement Learning - An Introduction. MIT Press, 1998.