Reward, punishment and internal expectation for training the random neural network with reinforcement

Date

1998-01-01

Author

Halıcı, Uğur

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The reinforcement learning scheme proposed in [8] for random neural networks [5] is based on reward and performs well for a stationary environment. However, when the environment is not stationary extinction becomes an important problem to be considered. In this paper, the reinforcement learning scheme is extended by introducing a weight update rule that takes into consideration the internal expectation of reinforcement. Such a scheme has made extinction possible while resulting in a good convergence to the most rewarding action.

URI

https://hdl.handle.net/11511/52892

Conference Name

13th International Symposium on Computer and Information Sciences (ISCIS 98)

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

U. Halıcı, “Reward, punishment and internal expectation for training the random neural network with reinforcement,” Antalya, Turkey, 1998, vol. 53, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52892.