Faster Policy Learning with Continuous-Time Gradients

• URL: http://arxiv.org/abs/2012.06684v1
• Date: Sat, 12 Dec 2020 00:22:56 GMT
• Title: Faster Policy Learning with Continuous-Time Gradients
• Authors: Samuel Ainsworth and Kendall Lowrey and John Thickstun and Zaid Harchaoui and Siddhartha Srinivasa
• Abstract summary: We study the estimation of policy gradients for continuous-time systems with known dynamics. By reframing policy learning in continuous-time, we show that it is possible construct a more efficient and accurate gradient estimator.
• Score: 6.457260875902829
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the estimation of policy gradients for continuous-time systems with known dynamics. By reframing policy learning in continuous-time, we show that it is possible construct a more efficient and accurate gradient estimator. The standard back-propagation through time estimator (BPTT) computes exact gradients for a crude discretization of the continuous-time system. In contrast, we approximate continuous-time gradients in the original system. With the explicit goal of estimating continuous-time gradients, we are able to discretize adaptively and construct a more efficient policy gradient estimator which we call the Continuous-Time Policy Gradient (CTPG). We show that replacing BPTT policy gradients with more efficient CTPG estimates results in faster and more robust learning in a variety of control tasks and simulators.

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