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Found 4 papers, 1 papers with code
Solving a Real-World Optimization Problem Using Proximal Policy Optimization with Curriculum Learning and Reward Engineering
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
It is sufficiently versatile to evaluate reinforcement learning algorithms on any real-world problem that fits our resource allocation framework.
Convex Optimization with an Interpolation-based Projection and its Application to Deep Learning
We then propose an optimization algorithm that follows the gradient of the composition of the objective and the projection and prove its convergence for linear objectives and arbitrary convex and Lipschitz domain defining inequality constraints.
SPI-GCN: A Simple Permutation-Invariant Graph Convolutional Network
In this work, we present a novel and simple convolutional neural network architecture for supervised learning on graphs that is provably invariant to node permutation.
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