Associative Reinforcement Learning Based on Continuous-Action Learning Automata

Associative reinforcement learning is a machine learning problem in an uncertain environment,where the goal of the learning system is to determine an optimal output action for each environmental state input.In this paper,a new continuous-action learning automaton(CALA)is proposed.The automaton uses a variable interval as its action set,and generates actions with uniform distribution over this interval.The end-points of the action-interval are adaptively updated according to the success/failure signals feedback from the environment.The proposed method is applied to solve two classical associative reinforcement learning tasks.Simulation results demonstrate the superiority of the new algorithm relative to two existing CALA algorithms.Compared with the old algorithms,the learning performance of the new algorithm can be improved on average by 1.9%to 5.7%,and at best by 22.4%to 65.2%.