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Reinforcement learning with internal expectation in the random neural networks for cascaded decisions
Date
2001-10-16
Author
Halıcı, Uğur
Metadata
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The reinforcement learning scheme proposed in Halici (J. Biosystems 40 (1997) 83) for the random neural network (RNN) (Neural Computation 1 (1989) 502) is based on reward and performs well for stationary environments. However, when the environment is not stationary it suffers from getting stuck to the previously learned action and extinction is not possible. To overcome the problem, the reinforcement scheme is extended in Halici (Eur. J. Oper. Res., 126(2000) 288) by introducing a new weight update rule (E-rule) which takes into consideration the internal expectation of reinforcement. Although the E-rule is proposed for the RNN, it can be used for training learning automata or other intelligent systems based on reinforcement learning. This paper looks into the behavior of the learning scheme with internal expectation for the environments where the reinforcement is obtained after a sequence of cascaded decisions. The simulation results have shown that the RNN learns well and extinction is possible even for the cases with several decision steps and with hundreds of possible decision paths.
Subject Keywords
General Biochemistry, Genetics and Molecular Biology
,
Modelling and Simulation
,
Statistics and Probability
,
Applied Mathematics
,
General Medicine
URI
https://hdl.handle.net/11511/49314
Journal
BioSystems
DOI
https://doi.org/10.1016/s0303-2647(01)00144-7
Collections
Department of Electrical and Electronics Engineering, Article