解析MuZero的隐藏状态，揭示其在游戏规划中的纠错机制与泛化差异。
Demystifying MuZero Planning: Interpreting the Learned Model
- 通过重建观测和状态一致性增强训练，解读模型学习到的隐藏状态
- 长时序模拟中模型精度下降，但规划过程能有效纠正误差
- 棋类游戏的隐状态学习效果优于Atari游戏，为算法改进提供方向
MuZero通过动态网络预测环境状态进行规划，在无需模拟器的情况下实现了多种游戏的超人表现。然而，其动态网络学习的隐状态使规划过程难以解释。本文通过引入观测重建和状态一致性机制，对9x9围棋、五子棋及Breakout、Ms. Pacman、Pong三款Atari游戏中的隐状态进行深入分析。结果表明，尽管动态网络在长时序模拟中准确性下降，但通过规划可有效修正误差；且在棋类游戏中隐状态学习更优。这些发现加深了对MuZero的理解，为提升其性能、鲁棒性和可解释性提供了新方向。代码与数据已公开。
原文摘要 · Abstract (English)
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.
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