Computer Science > Machine Learning
[Submitted on 13 Apr 2021 (v1), last revised 31 Mar 2022 (this version, v2)]
Title:Muesli: Combining Improvements in Policy Optimization
View PDFAbstract: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.
Submission history
From: Ivo Danihelka [view email][v1] Tue, 13 Apr 2021 13:04:29 UTC (812 KB)
[v2] Thu, 31 Mar 2022 09:35:40 UTC (804 KB)
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