Stochastic Genetic Algorithm-Assisted Fuzzy Q-Learning for Robotic Manipulators

This work proposes stochastic genetic algorithm-assisted Fuzzy Q-Learning-based robotic manipulator control. Specifically, the aim is to redefine the action choosing mechanism in Fuzzy Q-Learning for robotic manipulator control. Conventionally, a Fuzzy Q-Learning-based controller selects a deterministic action from available actions using fuzzy Q values. This deterministic Fuzzy Q-Learning is not an efficient approach, especially in dealing with highly coupled nonlinear systems such as robotic manipulators. Restricting the search for optimal action to the agent’s action set or a restricted set of Q values (deterministic) is a myopic idea. Herein, the proposal is to employ genetic algorithm as stochastic optimizer for action selection at each stage of Fuzzy Q-Learning-based controller. This turns out to be a highly effective way for robotic manipulator control rather than choosing an algebraic minimal action. As case studies, present work implements the proposed approach on two manipulators: (a) two-link arm manipulator and (b) selective compliance assembly robotic arm. Scheme is compared with baseline Fuzzy Q-Learning controller, Lyapunov Markov game-based controller and Linguistic Lyapunov Reinforcement Learning controller. Simulation results show that our stochastic genetic algorithm-assisted Fuzzy Q-Learning controller outperforms the above-mentioned controllers in terms of tracking errors along with lower torque requirements.