Shang-Hsuan Yang

Escaping from zero gradient: Revisiting action-constrained reinforcement learning via Frank-Wolfe policy optimization

Action-constrained reinforcement learning (RL) is a widely-used approach in various real-world applications, such as scheduling in networked systems with resource constraints and control of a robot with kinematic constraints. While the existing projection-based approaches ensure zero constraint viol…