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Problem definition: Supply chains are constantly evolving networks.Reinforcement learning is increasingly proposed as a solution to provideoptimal control of these networks. Academic/practical: However, learning incontinuously varying environments remains a challenge in the reinforcementlearning literature.Methodology: This paper therefore seeks to address whetheragents can control varying supply chain problems, transferring learning betweenenvironments that require different strategies and avoiding catastrophicforgetting of tasks that have not been seen in a while. To evaluate thisapproach, two state-of-the-art Reinforcement Learning (RL) algorithms arecompared: an actor-critic learner, Proximal Policy Optimisation(PPO), and aRecurrent Proximal Policy Optimisation (RPPO), PPO with a Long Short-TermMemory(LSTM) layer, which is showing popularity in online learningenvironments. Results: First these methods are compared on six sets ofenvironments with varying degrees of stochasticity. The results show that morelean strategies adopted in Batch environments are different from those adoptedin Stochastic environments with varying products. The methods are also comparedon various continuous supply chain scenarios, where the PPO agents are shown tobe able to adapt through continuous learning when the tasks are similar butshow more volatile performance when changing between the extreme tasks.However, the RPPO, with an ability to remember histories, is able to overcomethis to some extent and takes on a more realistic strategy. Managerialimplications: Our results provide a new perspective on the continuously varyingsupply chain, the cooperation and coordination of agents are crucial forimproving the overall performance in uncertain and semi-continuousnon-stationary supply chain environments without the need to retrain theenvironment as the demand changes.

Agent based modelling for continuously varying supply chains