QUANTUM INSPIRED REINFORCEMENT LEARNING IN CHANGING ENVIRONMENT

Abstract

Inspired by quantum theory and reinforcement learning, a new framework of learning in unknown probabilistic environment is proposed. Several simulated experiments are given; the results demonstrate the robustness of the new algorithm for some complex problems. Also we generalized the Grover algorithm to improve the rate of converging to an optimal path. In other words, the new generalized algorithm helps to increase the probability of selecting good actions with better weights' adjustments.