TR2019-154

QNTRPO: Including Curvature in TRPO


We propose a trust region method for policy optimization that employs QuasiNewton approximation for the Hessian, called Quasi-Newton Trust Region Policy Optimization (QNTRPO). Gradient descent is the de facto algorithm for reinforcement learning tasks with continuous controls. The algorithm has achieved state-of-the-art performance when used in reinforcement learning across a wide range of tasks. However, the algorithm suffers from a number of drawbacks including: lack of stepsize selection criterion, and slow convergence. We investigate the use of a trust region method using dogleg step and a Quasi-Newton approximation for the Hessian for policy optimization. We demonstrate through numerical experiments over a wide range of challenging continuous control tasks that our particular choice is efficient in terms of number of samples and improves performance

 

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  •  Jha, D., Raghunathan, A., Romeres, D., "Quasi-Newton Trust Region Policy Optimization", arXiv, December 2019.
    BibTeX arXiv
    • @article{Jha2019dec3,
    • author = {Jha, Devesh and Raghunathan, Arvind and Romeres, Diego},
    • title = {Quasi-Newton Trust Region Policy Optimization},
    • journal = {arXiv},
    • year = 2019,
    • month = dec,
    • url = {https://arxiv.org/abs/1912.11912}
    • }
  •  Jha, D., Raghunathan, A., Romeres, D., "Quasi-Newton Trust Region Policy Optimization", Conference on Robot Learning (CoRL), Leslie Pack Kaelbling, Danica Kragic, Komei Sugiura, Eds., October 2019, pp. 945-954.
    BibTeX TR2019-120 PDF Software
    • @inproceedings{Jha2019oct,
    • author = {Jha, Devesh and Raghunathan, Arvind and Romeres, Diego},
    • title = {Quasi-Newton Trust Region Policy Optimization},
    • booktitle = {Conference on Robot Learning (CoRL)},
    • year = 2019,
    • editor = {Leslie Pack Kaelbling, Danica Kragic, Komei Sugiura},
    • pages = {945--954},
    • month = oct,
    • publisher = {Proceedings of Machine Learning Research},
    • url = {https://www.merl.com/publications/TR2019-120}
    • }