Actor-critic driven deep reinforcement learning for optimising agri-food supply chain

Abstract

The agri-food supply chain is a complex network enclosing various stakeholders, from farmers to consumers, with multifaceted interactions and dependencies. Traditional supply chain management approaches often need help adapting to dynamic environments and optimising decision-making processes. Deep reinforcement learning is employed by integrating value-based and policy-based models, enhanced by advanced learning techniques, to tackle these challenges. This paper explores applying Deep Reinforcement Learning (DRL) approaches, including Q-learning, Deep Q-Learning (DQL), and the Actor-Critic method, to optimise the efficiency of the agri-food supply chain. The actor-critic model significantly enhances decision-making processes across various supply chain stages by improving efficiency and increasing profit margins. A specific scenario of sugar processing and distribution is incorporated, considering real-world scenarios to validate our model. DRL methods optimise sugar production, storage and distribution, ensuring timely deliveries and enhancing profitability. The models address fluctuating demand and transportation logistics challenges, resulting in a more streamlined and responsive sugar distribution network. The findings reveal that Actor-Critic and DQL methods significantly outperform traditional Q-learning considering product profitability, offering unique advantages in handling complex state-action spaces.