Demystifying MuZero Planning: Interpreting the Learned Model

TL;DR

This paper interprets MuZero's learned latent states, revealing how its planning process works and how the quality of these states varies across different game types, enhancing understanding and future improvements.

Contribution

It introduces observation reconstruction and state consistency into MuZero training and analyzes the learned states across multiple games to improve interpretability.

Findings

Dynamics network accuracy decreases over longer simulations.

MuZero effectively uses planning to correct errors despite inaccuracies.

Better latent states are learned in board games compared to Atari games.

Abstract

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.