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arXiv · 2104.06159

Muesli: Combining Improvements in Policy Optimization

Matteo Hessel·Ivo Danihelka·Fabio Viola·Arthur Guez·Simon Schmitt·Laurent Sifre·Theophane Weber·David Silver·Hado van Hasselt
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Abstract

We propose a novel policy update that combines regularized policy optimization with model learning as an auxiliary loss. The update (henceforth Muesli) matches MuZero's state-of-the-art performance on Atari. Notably, Muesli does so without using deep search: it acts directly with a policy network and has computation speed comparable to model-free baselines. The Atari results are complemented by extensive ablations, and by additional results on continuous control and 9x9 Go.

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Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theophane Weber, David Silver, Hado van Hasselt. 2021-04-13. Muesli: Combining Improvements in Policy Optimization. https://arxiv.org/abs/2104.06159

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