Interpreting the Learned Model in MuZero Planning

2024-11-07

arXiv.org

AI Summary

MuZero’s dynamics network relies on unobservable latent states for planning, undermining decision interpretability. Method: We propose an interpretability analysis framework that systematically evaluates latent state representation quality and dynamic modeling mechanisms—across Go, Gomoku, and Atari—using observation reconstruction loss and state consistency regularization. Contribution/Results: First, we find latent states achieve higher fidelity in board games but degrade in long-horizon Atari simulations. Second, the planning process itself exhibits intrinsic error-correction capability, mitigating distortions in latent state representations. Third, this self-correcting mechanism substantially enhances behavioral interpretability and robustness. Our findings provide novel insights into the internal operation of model-based reinforcement learning and establish a reproducible analytical paradigm for probing latent dynamics.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Machine Learning: Reinforcement Learning
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

MuZero has achieved superhuman performance in various games by using a dynamics network to predict 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 Outer-Open 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 playing performance, robustness, and interpretability of the MuZero algorithm.

Problem

• Interpreting opaque latent states in MuZero's dynamics network
• Evaluating latent state accuracy in board vs Atari games
• Improving MuZero's performance and interpretability through analysis

Innovation

• Incorporates observation reconstruction into MuZero training
• Uses state consistency for model interpretability
• Analyzes latent states in board and Atari games