This study introduces a novel approach for enhancing production decision-making by applying Reinforcement Learning to optimize the Economic Manufacturing Quantity (EMQ) model within discrete-time production-inventory systems. By incorporating machine status, inventory levels, and production choices, a Markov Decision Process (MDP) is constructed and combined with the Q-learning algorithm to derive an adaptive control method. This method enables the dynamic adaptation of production decisions, by effectively balancing the normal operation and shutdown for rest states. Numerical simulations show that the suggested Reinforcement Learning model surpasses conventional EMQ models and steady-state p... More >