Trust region-guided proximal policy optimization

Yuhui Wang, Hao He, Xiaoyang Tan, Yaozhong Gan

    Research output: Contribution to conferencePaperpeer-review

    24 Scopus citations


    Proximal policy optimization (PPO) is one of the most popular deep reinforcement learning (RL) methods, achieving state-of-the-art performance across a wide range of challenging tasks. However, as a model-free RL method, the success of PPO relies heavily on the effectiveness of its exploratory policy search. In this paper, we give an in-depth analysis on the exploration behavior of PPO, and show that PPO is prone to suffer from the risk of lack of exploration especially under the case of bad initialization, which may lead to the failure of training or being trapped in bad local optima. To address these issues, we proposed a novel policy optimization method, named Trust Region-Guided PPO (TRGPPO), which adaptively adjusts the clipping range within the trust region. We formally show that this method not only improves the exploration ability within the trust region but enjoys a better performance bound compared to the original PPO as well. Extensive experiments verify the advantage of the proposed method.

    Original languageEnglish (US)
    StatePublished - 2019
    Event33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019 - Vancouver, Canada
    Duration: Dec 8 2019Dec 14 2019


    Conference33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Information Systems
    • Signal Processing


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