Reinforcement learning approach to goal-regulation in a self-evolutionary manufacturing system

► We propose a goal-regulation mechanism that applies a reinforcement learning approach to autonomous goal-formation. ► Individual goals are regulated by a neural network-based fuzzy inference system called goal-regulation network (GRN). ► The GRN is updated by a reinforcement signal from another neural network called goal-evaluation network (GEN). ► The GEN approximates the compatibility of goals with current environmental situation. ► The proposed mechanism is validated by a simulation study on a production planning problem.