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    Proximal Policy Optimization on Casual Restaurant Raw Material Stock 

    Nutthawat Ekthammanit; ณัฐวัฒน์ เอกธรรมนิตย์; Worapol Pongpech; วรพล พงษ์เพ็ชร; National Institute of Development Administration. School of Applied Statistics; Worapol Pongpech; วรพล พงษ์เพ็ชร (National Institute of Development Administration, 13/8/2021)

    This research focuses on the Proximal Policy Optimization Algorithm (PPO) of Reinforcement Learning to make a forecasting model of raw material stock in restaurants.  The restaurant's daily raw material stock ordered and the number of raw material stock used fluctuates daily. The unused raw material stock is left as wasted material. It caused fermentation and produced methane gas that rises to destroy ozone into the atmosphere. This research investigated a One-Attribute Model and a Multi-Attribute Model. The dataset used in this research is ...