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.

这项工作提出了基于随机遗传算法的基于模糊Q学习的机器人操纵器控制。具体而言，其目的是重新定义模糊Q学习中用于机械手控制的动作选择机制。常规地，基于模糊Q学习的控制器使用模糊Q值从可用动作中选择确定性动作。这种确定性的模糊Q学习并不是一种有效的方法，尤其是在处理高度耦合的非线性系统（例如机器人操纵器）时。将针对最佳操作的搜索限制为座席的操作集或受限的Q集价值观（确定性）是一种近视思想。在此，建议采用遗传算法作为随机优化器，用于基于模糊Q学习的控制器的每个阶段的动作选择。事实证明，这是用于机器人操纵器控制的高效方法，而不是选择代数最小动作。作为案例研究，当前的工作在两个操纵器上实现了建议的方法：（a）两连杆臂操纵器和（b）选择性依从性装配机械臂。将方案与基线模糊Q学习控制器，基于Lyapunov Markov游戏的控制器和语言Lyapunov强化学习控制器进行比较。仿真结果表明，我们的随机遗传算法辅助的模糊Q-学习控制器在跟踪误差以及较低的扭矩要求方面均优于上述控制器。