Supervised policy update for deep reinforcement learning

Quan Vuong, Keith Ross, Yiming Zhang

    Research output: Contribution to conferencePaper

    Abstract

    We propose a new sample-efficient methodology, called Supervised Policy Update (SPU), for deep reinforcement learning. Starting with data generated by the current policy, SPU formulates and solves a constrained optimization problem in the non-parameterized proximal policy space. Using supervised regression, it then converts the optimal non-parameterized policy to a parameterized policy, from which it draws new samples. The methodology is general in that it applies to both discrete and continuous action spaces, and can handle a wide variety of proximity constraints for the non-parameterized optimization problem. We show how the Natural Policy Gradient and Trust Region Policy Optimization (NPG/TRPO) problems, and the Proximal Policy Optimization (PPO) problem can be addressed by this methodology. The SPU implementation is much simpler than TRPO. In terms of sample efficiency, our extensive experiments show SPU outperforms TRPO in Mujoco simulated robotic tasks and outperforms PPO in Atari video game tasks.

    Original languageEnglish (US)
    StatePublished - Jan 1 2019
    Event7th International Conference on Learning Representations, ICLR 2019 - New Orleans, United States
    Duration: May 6 2019May 9 2019

    Conference

    Conference7th International Conference on Learning Representations, ICLR 2019
    CountryUnited States
    CityNew Orleans
    Period5/6/195/9/19

    ASJC Scopus subject areas

    • Education
    • Computer Science Applications
    • Linguistics and Language
    • Language and Linguistics

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  • Cite this

    Vuong, Q., Ross, K., & Zhang, Y. (2019). Supervised policy update for deep reinforcement learning. Paper presented at 7th International Conference on Learning Representations, ICLR 2019, New Orleans, United States.