Using homomorphisms to transfer options across continuous reinforcement learning domains

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2006, Pages 494-499

  Soni, Vishal ^a   Singh, Satinder ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BOOTSTRAP LEARNING; HOMOMORPHISMS; MARKOV DECISION PROCESS (MDP); REINFORCEMENT LEARNING;

CONFORMAL MAPPING; DATA TRANSFER; KNOWLEDGE ACQUISITION; MARKOV PROCESSES; MATHEMATICAL MODELS; PROBLEM SOLVING;

LEARNING SYSTEMS;

EID: 33750690679     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. Dean, T., and Givan, R. 1997. Model minimization in markov decision processes. In American Association for Artificial Intelligence, 106-111.
2. Guestrin, C.; Koller, D.; Parr, R.; and Venkataraman, S. 2003. Efficient solution algorithms for factored MDPs. Journal of Artificial Intelligence Research 19:399-468.
3. Ravindran, B., and Barto, A. G. 2002. Model minimization in hierarchical reinforcement learning. In Fifth Symposium on Abstraction, Reformulation and Approximation, 196-211.
4. Ravindran, B., and Barto, A. G. 2003. An algebraic approach to abstraction in reinforcement learning. In Twelfth Yale Workshop on Adaptive and Learning Systems, 109-144. Yale University.
5. Stone, P.; Sutton, R. S.; and Kuhlmann, G. 2005. Reinforcement learning for robocup-soccer keepaway. Adaptive Behavior 13(3):165-188.
6. Sutton, R., and Barto, A. G. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press.
7. Sutton, R. S.; Precup, D.; and Singh, S. P. 1998. Intraoption learning about temporally abstract actions. In International Conference on Machine Learning, 556-564.
8. Sutton, R. S.; Precup, D.; and Singh, S. P. 1999. Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning. Artificial Intelligence. 112(1-2):181-211.
9. Taylor, M. E., and Stone, P. 2005. Behavior transfer for value-function-based reinforcement learning. In The Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, 53-59.