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Solving a Real-World Optimization Problem Using Proximal Policy Optimization with Curriculum Learning and Reward Engineering
April 4, 2024, 4:41 a.m. | Abhijeet Pendyala, Asma Atamna, Tobias Glasmachers
cs.LG updates on arXiv.org arxiv.org
Abstract: We present a proximal policy optimization (PPO) agent trained through curriculum learning (CL) principles and meticulous reward engineering to optimize a real-world high-throughput waste sorting facility. Our work addresses the challenge of effectively balancing the competing objectives of operational safety, volume optimization, and minimizing resource usage. A vanilla agent trained from scratch on these multiple criteria fails to solve the problem due to its inherent complexities. This problem is particularly difficult due to the environment's …
abstract agent arxiv challenge cs.lg curriculum curriculum learning engineering facility optimization policy ppo safety sorting through type waste work world
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