Demystifying MuZero Planning: Interpreting the Learned Model
This work addresses interpretability issues in MuZero for researchers and practitioners, but it is incremental as it analyzes and provides insights without introducing major new methods.
The paper tackled the problem of interpreting the opaque latent states learned by MuZero's dynamics network in planning, and found that while the network's accuracy decreases over longer simulations, MuZero effectively corrects errors through planning, with better latent states learned in board games than in Atari games.
MuZero has achieved superhuman performance in various games by using a dynamics network to predict the environment dynamics for planning, without relying on simulators. However, the latent states learned by the dynamics network make its planning process opaque. This paper aims to demystify MuZero's model by interpreting the learned latent states. We incorporate observation reconstruction and state consistency into MuZero training and conduct an in-depth analysis to evaluate latent states across two board games: 9x9 Go and Gomoku, and three Atari games: Breakout, Ms. Pacman, and Pong. Our findings reveal that while the dynamics network becomes less accurate over longer simulations, MuZero still performs effectively by using planning to correct errors. Our experiments also show that the dynamics network learns better latent states in board games than in Atari games. These insights contribute to a better understanding of MuZero and offer directions for future research to improve the performance, robustness, and interpretability of the MuZero algorithm. The code and data are available at https://rlg.iis.sinica.edu.tw/papers/demystifying-muzero-planning.