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Muesli: Combining Improvements in Policy Optimization
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Paper
Muesli: Combining Improvements in Policy Optimization
2022
Overview
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.
Publisher
Cornell University Library, arXiv.org
Subject
