Summary of Solving a Real-world Optimization Problem Using Proximal Policy Optimization with Curriculum Learning and Reward Engineering, by Abhijeet Pendyala et al.
Solving a Real-World Optimization Problem Using Proximal Policy Optimization with Curriculum Learning and Reward Engineering
by Abhijeet Pendyala, Asma Atamna, Tobias Glasmachers
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This AI research paper presents a proximal policy optimization (PPO) agent trained using curriculum learning (CL) principles and meticulous reward engineering to optimize a real-world high-throughput waste sorting facility. The agent balances competing objectives of operational safety, volume optimization, and minimizing resource usage. A vanilla agent fails to solve the problem due to complexities, delayed rewards with long time horizons, and class imbalance. Our five-stage CL approach iteratively refines the reward mechanism and adapts to environmental dynamics, enabling the agent to learn an optimal policy. Results demonstrate significant improvements in inference-time safety and waste sorting plant efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This AI research paper helps a big facility sort trash better by training a computer program using special techniques. The program must balance three things: keeping people safe, getting rid of as much trash as possible, and using the least amount of resources. A normal program wouldn’t be able to do this because it’s too hard. This new program uses a special way of learning called curriculum learning, which makes it better at sorting trash. The program gets better at its job by gradually learning more complex things and getting feedback on how well it’s doing. As a result, the program is much safer and better at sorting trash. |
Keywords
* Artificial intelligence * Curriculum learning * Inference * Optimization
