Neural PPO-Clip Attains Global Optimality: A Hinge Loss Perspective

no code implementations • 26 Oct 2021 • Nai-Chieh Huang, Ping-Chun Hsieh, Kuo-Hao Ho, Hsuan-Yu Yao, Kai-Chun Hu, Liang-Chun Ouyang, I-Chen Wu

Policy optimization is a fundamental principle for designing reinforcement learning algorithms, and one example is the proximal policy optimization algorithm with a clipped surrogate objective (PPO-Clip), which has been popularly used in deep reinforcement learning due to its simplicity and effectiveness.

reinforcement-learning Reinforcement Learning (RL)