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arXiv · 2006.07554

Online Hyper-parameter Tuning in Off-policy Learning via Evolutionary Strategies

Yunhao Tang·Krzysztof Choromanski
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Abstract

Off-policy learning algorithms have been known to be sensitive to the choice of hyper-parameters. However, unlike near on-policy algorithms for which hyper-parameters could be optimized via e.g. meta-gradients, similar techniques could not be straightforwardly applied to off-policy learning. In this work, we propose a framework which entails the application of Evolutionary Strategies to online hyper-parameter tuning in off-policy learning. Our formulation draws close connections to meta-gradients and leverages the strengths of black-box optimization with relatively low-dimensional search spaces. We show that our method outperforms state-of-the-art off-policy learning baselines with static hyper-parameters and recent prior work over a wide range of continuous control benchmarks.

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Yunhao Tang, Krzysztof Choromanski. 2020-06-13. Online Hyper-parameter Tuning in Off-policy Learning via Evolutionary Strategies. https://arxiv.org/abs/2006.07554

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