Poster

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

Keywords: [ Reinforcement Learning and Planning -> Deep RL ]

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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