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


Halici U.

13th International Symposium on Computer and Information Sciences (ISCIS 98), BELEK ANTALYA, Turkey, 26 - 28 October 1998, vol.53, pp.162-169 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 53
  • City: BELEK ANTALYA
  • Country: Turkey
  • Page Numbers: pp.162-169

Abstract

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.