A reinforcement learning adaptive fuzzy controller for robots

摘要

In this paper, a new reinforcement learning scheme is developed for a class of serial-link robot arms. Traditional reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic environment. In the proposed reinforcement learning scheme, an agent is employed to collect signals from a fixed gain controller, an adaptive critic element and a fuzzy action-generating element. The action generating clement is a fuzzy approximator with a set of tunable parameters, and the performance measurement mechanism sends an error metric to the adaptive critic element for generating and transferring a reinforcement learning signal to the agent. Moreover, a tuning algorithm of the proposed scheme that can guarantee both tracking performance and stability is derived from the Lyapunov stability theory. Therefore, a combination of adaptive fuzzy control and reinforcement learning scheme is also concerned with algorithms for eliminating a sequence of decisions from experience. Simulations of the proposed reinforcement adaptive fuzzy control scheme on the cart-pole balancing problem and a two-degree-of freedom (2DOF) manipulator, SCARA robot arm verify the effectiveness of our approach.