Policy Transition of Reinforcement Learning for an Agent Based SCM System

Reinforcement learning (RL) is successfully applied to some dynamical and unpredictable domains. The Supply Chain Management (SCM) is NP-hard problem. Some proposed RL methods perform better than traditional tools for dynamic problem solving in SCM. It realizes on-line learning and performs efficiently in some applications, but RL agent reacts worse than some heuristic methods to sudden changes in SCM demand since the trial-and-error characteristic of RL is time-consuming in practice. By surveying an efficient policy transition mechanism in RL about how to mapping existing policies in the previous task to a new policies in a changed task, this paper proposes a novel RL agent based SCM system that decreases learning time of the RL agent to a dynamic environment. As the result, the RL agent derives the maximal profit using RL technique as jobs coming with a stable distribution. Further, the RL agent makes the optimal procurement satisfying the requirement of sudden changes in the supply chain network by the policy transition mechanism.