A fuzzy reinforcement learning algorithm for inventory control in supply chains

In the real world, applications with very large state and action spaces and unknown state transition probability, classical reinforcement learning algorithms usually show poor performance. One way to address the performance problem is to approximate the policy or value function. Fuzzy rule-based systems are amongst the well-known function approximators. This paper presents a Flexible Fuzzy Reinforcement Learning algorithm, in which value function is approximated by a fuzzy rule-based system. The proposed algorithm has a separate module for tuning the structure of fuzzy rules. Moreover, the parameters of the system are tuned during the learning phase. Next, the proposed algorithm is applied to the problem of inventory control in supply chains. In this problem, a fuzzy agent (supplier) should determine the amount of orders for each retailer based on their utility for supplier, by considering its limited supply capacity. Finally, a simulation is performed to show the capability of the proposed algorithm.