Economic Hierarchical Q-Learning

Proceedings of the National Conference on Artificial Intelligence

Volumn 2, Issue , 2008, Pages 689-695

  Schultink, Erik G ^a   Cavallo, Ruggiero ^a   Parkes, David C ^a  

^a HARVARD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIONICS; EDUCATION; REINFORCEMENT;

DATA EXCHANGES; EMPIRICAL RESULTS; HIERARCHICAL STATES; LEARNING FRAMEWORKS; OPTIMAL POLICIES; OPTIMALITY; OTHER ALGORITHMS; RELATIVE VALUES; RELEVANT INFORMATIONS; SUBOPTIMALITY;

LEARNING ALGORITHMS;

EID: 57749206785     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

1. Andre, D., and Russell, S. 2002. State abstraction for programmable reinforcement learning agents. In AAAI-02. Edmonton, Alberta: AAAI Press.
2. Baum, E. B., and Durdanovich, I. 1998. Evolution of cooperative problem-solving in an artificial economy. Journal of Arti cial Intelligence Research.
3. Dean, T., and Lin, S.-H. 1995. Decomposition techniques for planning in stochastic domains. In IJCAI-95, 1121-1127. San Francisco, CA: Morgan Kaufmann Publishers.
4. Dietterich, T. G. 2000a. Hierarchical reinforcement learning with MAXQ value function decomposition. Journal of Arti cial Intelligence Research 13: 227-303.
5. Dietterich, T. G. 2000b. State abstraction in MAXQ hierarchical reinforcement learning. Advances in Neural Information Processing Systems 12: 994-1000.
6. Holland, J. 1986. Escaping brittleness: The possibilities of general purpose learning algorithms applied to parallel rule-based systems. In Machine Learning, volume 2. San Mateo, CA: Morgan Kaufmann.
7. Marthi, B.; Russell, S.; and Andre, D. 2006. A compact, hierarchically optimal Q-function decomposition. In UAI- 06.
8. Parr, R., and Russell, S. 1998. Reinforcement learning with hierarchies of machines. Advances in Neural Information Processing Systems 10.