LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios

TL;DR

LightZero introduces a comprehensive benchmark for applying Monte Carlo Tree Search and MuZero algorithms across diverse real-world decision-making scenarios, addressing previous limitations in complex, stochastic, and large action space environments.

Contribution

This work presents the first unified benchmark for MCTS/MuZero in general decision scenarios, decomposes algorithm design into modular components, and demonstrates improved agent performance across multiple domains.

Findings

Enhanced exploration and optimization strategies improve agent performance.

LightZero agents outperform baselines in various environments.

Benchmark results show scalability and efficiency of the methods.

Abstract

Building agents based on tree-search planning capabilities with learned models has achieved remarkable success in classic decision-making problems, such as Go and Atari. However, it has been deemed challenging or even infeasible to extend Monte Carlo Tree Search (MCTS) based algorithms to diverse real-world applications, especially when these environments involve complex action spaces and significant simulation costs, or inherent stochasticity. In this work, we introduce LightZero, the first unified benchmark for deploying MCTS/MuZero in general sequential decision scenarios. Specificially, we summarize the most critical challenges in designing a general MCTS-style decision-making solver, then decompose the tightly-coupled algorithm and system design of tree-search RL methods into distinct sub-modules. By incorporating more appropriate exploration and optimization strategies, we can significantly enhance these sub-modules and construct powerful LightZero agents to tackle tasks across a wide range of domains, such as board games, Atari, MuJoCo, MiniGrid and GoBigger. Detailed benchmark results reveal the significant potential of such methods in building scalable and efficient decision intelligence. The code is available as part of OpenDILab at https://github.com/opendilab/LightZero.