Robust, efficient, globally-optimized reinforcement learning with the parti-game algorithm

Advances in Neural Information Processing Systems

Volumn , Issue , 1999, Pages 961-967

  Al Ansari, Mohammad A ^a   Williams, Ronald J ^a  

^a NORTHEASTERN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

REINFORCEMENT LEARNING;

CURSE OF DIMENSIONALITY; HIGH-DIMENSIONAL PROBLEMS; NONLOCAL;

ALGORITHMS;

EID: 72949120761     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (5)

1. Al-Ansari, M. A. and R. J. Williams (1998). Modifying the parti-game algorithm for increased robustness, higher efficiency and better policies. Technical Report NU-CCS-98-13, College of Computer Science, Northeastern University, Boston, MA.
2. Moore, A. (1994a). Variable resolution reinforcement learning. In Proceedings of the Eighth Yale Workshop on Adaptive and Learning Systems. Center for Systems Science, Yale University.
3. Moore, A. W. (1994b). The parti-game algorithm for variable resolution reinforcement learning in multidimensional state spaces. In Proceedings of Neural Information Processing Systems Conference 6. Morgan Kaufman.
4. Moore, A. W. and C. G. Atkeson (1995). The parti-game algorithm for variable resolution reinforcement learning in multidimensional state-spaces. Machine Learning 21.
5. Sutton, R. S. and A. G. Barto (1998). Reinforcement Learning: An Introduction. MIT Press.