Solving a Real-World Optimization Problem Using Proximal Policy Optimization with Curriculum Learning and Reward Engineering

no code implementations • 3 Apr 2024 • Abhijeet Pendyala, Asma Atamna, Tobias Glasmachers

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.

ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocation

1 code implementation • 6 Jul 2023 • Abhijeet Pendyala, Justin Dettmer, Tobias Glasmachers, Asma Atamna

It is sufficiently versatile to evaluate reinforcement learning algorithms on any real-world problem that fits our resource allocation framework.

Decision Making reinforcement-learning