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Demystifying MuZero Planning: Interpreting the Learned Model
Created by
Haebom
Author
Hung Guei, Yan-Ru Ju, Wei-Yu Chen, Ti-Rong Wu
Outline
MuZero has achieved superhuman performance on a variety of games using dynamical networks that predict environmental dynamics without a simulator. However, the latent states learned by the dynamical networks make the planning process opaque. In this paper, we integrate observation reconstruction and state consistency into MuZero learning, and conduct an in-depth analysis to evaluate the latent states on two board games, 9x9 Go and Gomoku, and three Atari games, Breakout, Ms. Pacman, and Pong, to interpret the MuZero model. Experimental results show that while the dynamical networks are less accurate in longer simulations, MuZero performs effectively by correcting errors through planning. We also show that the dynamical networks learn better latent states in board games than in Atari games. These insights provide directions for future research to deepen our understanding of MuZero and improve the performance, robustness, and interpretability of the MuZero algorithm. Code and data are available at https://rlg.iis.sinica.edu.tw/papers/demystifying-muzero-planning .
MuZero's latent state analysis has improved our understanding of how the model works.
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We show that MuZero works effectively by compensating for errors through planning, even when the accuracy of the dynamical network decreases over long simulations.
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We demonstrate that MuZero's dynamical network learns latent states better in board games than in Atari games.
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We suggest future research directions to improve the performance, robustness, and interpretability of the MuZero algorithm.
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Limitations:
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The types of games analyzed are limited (9x9 Go, Gomoku, Breakout, Ms. Pacman, Pong).
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Further research is needed to explore generalizability to more diverse and complex game environments.
