SELF-SUPERVISED LEARNING-MODEL

This paper describes a reinforcement learning algorithm based on super vised learning. Many of reinforcement algorithms use associative searc h to discover and learn actions that make the system perform a desired task. One problem with associative search is that the system's action s are often inconsistent. In the searching process, the system's actio ns are always decided stochastically. Therefore, the system cannot per form learned actions more than once. To solve this problem, this algor ithm uses a neural network which can predict an evaluation of an actio n and control the influence of the stochastic element. The effectivene ss of this algorithm was checked by computer simulations.