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

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.