Trust Region Policy Optimization

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John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, Philipp Moritz ;
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1889-1897, 2015.

Abstract

In this article, we describe a method for optimizing control policies, with guaranteed monotonic improvement. By making several approximations to the theoretically-justified scheme, we develop a practical algorithm, called Trust Region Policy Optimization (TRPO). This algorithm is effective for optimizing large nonlinear policies such as neural networks. Our experiments demonstrate its robust performance on a wide variety of tasks: learning simulated robotic swimming, hopping, and walking gaits; and playing Atari games using images of the screen as input. Despite its approximations that deviate from the theory, TRPO tends to give monotonic improvement, with little tuning of hyperparameters.

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