Multi-agent reinforcement learning based maintenance policy for a resource constrained flow line system

This paper investigates the maintenance problem for a flow line system consisting of two series machines with an intermediate finite buffer in between. Both machines independently deteriorate as they operate, resulting in multiple yield levels. Resource constrained imperfect preventive maintenance actions may bring the machine back to a better state. The problem is modeled as a semi-Markov decision process. A distributed multi-agent reinforcement learning algorithm is proposed to solve the problem and to obtain the control-limit maintenance policy for each machine associated with the observed state represented by yield level and buffer level. An asynchronous updating rule is used in the learning process since the state transitions of both machines are not synchronous. Experimental study is conducted to evaluate the efficiency of the proposed algorithm.