REAL-TIME PRODUCTION SCHEDULING WITH DEEP REINFORCEMENT LEARNING AND MONTE CARLO TREE SEARCH

摘要

Systems and methods provide real-time production scheduling by integrating deep reinforcement learning and Monte Carlo tree search. A manufacturing process simulator is used to train a deep reinforcement learning agent to identify the sub-optimal policies for a production schedule. A Monte Carlo tree search agent is implemented to speed up the search for near-optimal policies of higher quality from the sub-optimal policies.