Constrained Policy Optimization via Sampling-Based Weight-Space Projection

This article likely presents a novel approach to constrained policy optimization, a crucial area in reinforcement learning. The use of sampling-based weight-space projection suggests a method for efficiently handling constraints during the optimization process. The source being ArXiv indicates this is a research paper, likely detailing the methodology, experiments, and results.