Reinforcement learning in random neural networks for cascaded decisions

BioSystems

Volumn 40, Issue 1-2, 1997, Pages 83-91

  Halici, Ugur ^a  

^a MIDDLE EAST TECHNICAL UNIVERSITY   (Turkey)

Author keywords

Mazes; Random neural networks; Recency effect; Reinforcement learning

Indexed keywords

CELL ACTIVITY; COMPUTER MODEL; CONFERENCE PAPER; DECISION MAKING; ELECTRIC POTENTIAL; INSTRUMENTAL CONDITIONING; LEARNING; MAZE TEST; NERVE CELL; REINFORCEMENT; SPIKE;

EID: 0031060284     PISSN: 03032647     EISSN: None     Source Type: Journal    
DOI: 10.1016/0303-2647(96)01633-4     Document Type: Conference Paper

References (17)

1. Baddaley, A., 1982, Your Memory, Pelican Book, UK
2. Barto, A.C., Sutton, R.S. and Watkins, C.J.C.H., 1989, Learning and Sequential Decision Making, COINS Technical Report.
3. Carlson, N.R., 1977, Physiology of Behaviour, Allyn and Bacon.
4. Gelenbe, E., 1989, Random neural networks with negative and positive signals and product form solution. Neural Comput. 1(4), 502-510.
5. Gelenbe, E., 1990, Stability of the random neural network model. Neural Comput. 2(2), 239-247.
6. Gelenbe, E., 1993, Learning in the recurrent random neural network. Neural Computation 5(1), 154-164.
7. Halici, U. and Yaranli, U., 1992, Neural Networks in Mazes, in: Proc. of IEEE-INNS International Joint Conference on Neural Networks. Beijing, China, November, Vol-II, 711-716.
8. Klopf, A.H., 1974, Brain Function and Adaptive Systems, in: Proc. of IEEE International Conference on Systems, Man Cybernetics.
9. Madenoglu, A., 1994, Simulation results for Connectionist Maze Learning, B.Sc. Project Report, Dept. of Electrical Engineering, METU.
10. Narendra, K. and Thathachar M.A.L., 1989, Learning Automata: An Introduction, Prentice Hall, Englewood Cliffs.
11. Reynolds, A.G. and Flagg, P.W., 1977, Cognitive Psychology, Wintrop Publishers.
12. Szepesvary, C., 1995, A general framework for reinforcement learning, in: Proc. of International Conference on Artificial neural Networks, Paris, II, 165-170.
13. Sutton, R.S., 1984, Temporal Credit Assignment in Reinforcement Learning. Doctoral Dissertation, University of Massachusetts, Amherst.
14. Sutton, R.S., 1988, Learning to predict by the methods of temporal difference. Machine Learn. 3, 9-44.
15. Tsetlin, M.L., 1973, Automaton Theory and Modelling Biological Systems, Academic Press.
16. Watkins, C. and Dayan, P., 1992, Technical Note: Q learning. Machine Learn. 8, 55-68.
17. Zhang, P. and Canu, S., 1995, Indirect Adaptive Explorations in Entropy-based Reinforcement Learning, in: Proc. of International Conference on Artificial Neural Networks, Paris, II, 171-176.