揭秘 MuZero 规划：解释已学习的模型
Demystifying MuZero Planning: Interpreting the Learned Model
AI总结：
本文通过观测重建和状态一致性训练来解释 MuZero 学习到的潜在状态，揭示其在长模拟中虽动力学精度下降但仍能通过规划纠错，并发现棋盘游戏中的潜在状态优于 Atari 游戏。
摘要：
MuZero 通过使用动力学网络预测环境动态以进行规划，无需依赖模拟器，已在多种游戏中取得超越人类的表现。然而，动力学网络学习到的潜在状态使其规划过程不透明。本文旨在通过解释学习到的潜在状态来揭秘 MuZero 的模型。我们将观测重建和状态一致性纳入 MuZero 训练，并进行深入分析，以评估两类棋盘游戏（9x9 围棋和五子棋）和三个 Atari 游戏（Breakout、Ms. Pacman 和 Pong）中的潜在状态。我们的发现表明，虽然动力学网络在较长的模拟中变得不太准确，但 MuZero 仍然通过使用规划来纠正错误而有效运行。我们的实验还表明，动力学网络在棋盘游戏中学习到的潜在状态优于在 Atari 游戏中学习到的潜在状态。这些见解有助于更好地理解 MuZero，并为未来研究提高 MuZero 算法的性能、鲁棒性和可解释性提供了方向。代码和数据可在 https://rlg.iis.sinica.edu.tw/papers/demystifying-muzero-planning 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
