Speeding up the parti-game algorithm

Advances in Neural Information Processing Systems

Volumn , Issue , 2003, Pages

  Likhachev, Maxim ^a   Koenig, Sven ^b  

^a Carnegie Mellon University   (United States)

^b Georgia Institute of Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONTINUOUS DOMAIN; DYNAMIC DISCRETIZATION; EFFICIENT IMPLEMENTATION; HIGH-DIMENSIONAL; MINIMAX; REPLANNING ALGORITHM; SEARCH ALGORITHMS;

ALGORITHMS;

EID: 79958276619     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. *. In T. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, Cambridge, MA, 2002. MIT Press.
2. D. Frigioni, A. Marchetti-Spaccamela, and U. Nanni. Fully dynamic algorithms for maintaining shortest paths trees. Journal of Algorithms, 34(2): 251-281, 2000.
3. A. Moore and C. Atkeson. The parti-game algorithm for variable resolution reinforcement learning in multidimensional state-spaces. Machine Learning, 21(3): 199-233, 1995.
4. A. Moore and C. Atkeson. Prioritized sweeping: Reinforcement learning with less data and less time. Machine Learning, 13(1): 103-130, 1993.
5. M. Likhachev and S. Koenig. Speeding up reinforcement learning with incremental heuristic minimax search. Technical Report GIT-COGSCI-2002/5, College of Computing, Georgia Institute of Technology, Atlanta (Georgia), 2002.
6. M. Al-Ansari. Efficient Reinforcement Learning in Continuous Environments. PhD thesis, College of Computer Science, Northeastern University, Boston (Massachusetts), 2001.
7. G. Ramalingam and T. Reps. An incremental algorithm for a generalization of the shortest-path problem. Journal of Algorithms, 21: 267-305, 1996.