Robotic Packaging Optimization with Reinforcement Learning
A reinforcement learning framework was developed to optimize a box conveyor belt speed in automated food packaging systems when product supply varies. The agent learns to balance maximizing throughput against quality constraints, such as ensuring high packing rates and preventing empty boxes, by using a reward function that penalizes lost products and encourages smooth speed changes.