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arXiv · 1912.11912

Quasi-Newton Trust Region Policy Optimization

Devesh Jha·Arvind Raghunathan·Diego Romeres
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Abstract

We propose a trust region method for policy optimization that employs Quasi-Newton 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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Devesh Jha, Arvind Raghunathan, Diego Romeres. 2019-12-26. Quasi-Newton Trust Region Policy Optimization. https://arxiv.org/abs/1912.11912

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