Hinge Policy Optimization: Rethinking Policy Improvement and Reinterpreting PPO

• URL: http://arxiv.org/abs/2110.13799v1
• Date: Tue, 26 Oct 2021 15:56:57 GMT
• Title: Hinge Policy Optimization: Rethinking Policy Improvement and Reinterpreting PPO
• Authors: Hsuan-Yu Yao, Ping-Chun Hsieh, Kuo-Hao Ho, Kai-Chun Hu, Liang-Chun Ouyang, I-Chen Wu
• Abstract summary: Policy optimization is a fundamental principle for designing reinforcement learning algorithms. Despite its superior empirical performance, PPO-clip has not been justified via theoretical proof up to date. This is the first ever that can prove global convergence to an optimal policy for a variant of PPO-clip.
• Score: 6.33198867705718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Despite its superior empirical performance, PPO-clip has not been justified via theoretical proof up to date. This paper proposes to rethink policy optimization and reinterpret the theory of PPO-clip based on hinge policy optimization (HPO), called to improve policy by hinge loss in this paper. Specifically, we first identify sufficient conditions of state-wise policy improvement and then rethink policy update as solving a large-margin classification problem with hinge loss. By leveraging various types of classifiers, the proposed design opens up a whole new family of policy-based algorithms, including the PPO-clip as a special case. Based on this construct, we prove that these algorithms asymptotically attain a globally optimal policy. To our knowledge, this is the first ever that can prove global convergence to an optimal policy for a variant of PPO-clip. We corroborate the performance of a variety of HPO algorithms through experiments and an ablation study.

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