Reinforcement learning in random neural networks for cascaded decisions

The Random Neural Network (RNN) model, in which signals travel as voltage spikes rather than as fixed signal levels, represents more closely the manner in which signals are transmitted in biophysical neural networks. In this paper a reinforcement learning strategy is proposed to make a sequence of cascaded decisions to achieve a goal while aiming to optimize the total cost of the cascaded decisions. For this purpose, RANs are used to model the system and a weight update rule together with a reinforcement function is provided. The performance of the learning strategy is analysed by applying it to the maze learning problem. The simulation results show that the performance of the system is highly dependent on the chosen reinforcement function and quite satisfactory results are obtained when the reinforcement function takes the recency effect into consideration.