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

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.

• The paper introduces LightZero, unifying nine MCTS variants and evaluating them across 20+ environments to tackle real-world decision challenges.
• It proposes a decoupled training pipeline with four core modules, enhancing modularity, scalability, and integration of new strategies.
• Experimental insights reveal that self-supervised and intrinsic reward techniques significantly boost performance in complex, high-cost simulation settings.

Overview of LightZero: A Unified Benchmark for Monte Carlo Tree Search

The paper presents LightZero, a comprehensive benchmark designed to extend the applicability of Monte Carlo Tree Search (MCTS) in varied sequential decision-making environments. The benchmarking initiative aims to address the limitations of traditional MCTS algorithms, particularly their challenges in real-world applications characterized by complex action spaces, high simulation costs, and environment stochasticity.

Key Contributions

1. Unified Benchmark: LightZero integrates nine branches of MCTS/MuZero algorithms, assessing them across over 20 diverse environments. This includes board games, Atari, MuJoCo, MiniGrid, and GoBigger.
2. Decoupled Training Pipeline: The paper introduces a modular training architecture, emphasizing the decomposition of tightly-coupled algorithmic structures. LightZero's framework consists of four core sub-modules: data collector, data arranger, agent learner, and agent evaluator, facilitating the integration of novel strategies and enhancing scalability.
3. Algorithmic Enhancements: LightZero incorporates advanced exploration and optimization strategies, such as self-supervised representation learning and intrinsic reward mechanisms, to address exploration and model alignment challenges.

Challenges in MCTS

The authors identify six primary challenges that need addressing for the design of a general MCTS algorithm:

• Multi-Modal Observation Spaces: Handling diverse data representations.
• Complex Action Spaces: Generating diverse decision signals for discrete, continuous, and hybrid actions.
• Inherent Stochasticity: Managing uncertainty in environment dynamics and state spaces.
• Reliance on Prior Knowledge: Reducing the dependency on environment-specific information.
• Simulation Cost: Mitigating the time demands of extensive simulations.
• Hard Exploration: Ensuring efficient policy exploration in sparse reward settings.

Experimental Insights

The study highlights several observations:

• Self-Supervised Learning: Incorporating self-supervised loss significantly enhances performance in image-input environments like Atari.
• Complex Action Spaces: The Gaussian policy representation in Sampled MuZero demonstrates scalability in continuous spaces.
• Efficient Exploration: Intrinsic exploration techniques are shown to address exploration deficiencies effectively.

Future Directions

The paper posits potential future improvements, such as integrating LightZero with LLMs and model-based RL techniques. These integrations could enhance decision intelligence in more sophisticated scenarios.

Conclusion

LightZero marks a substantial effort toward broadening the applicability of MCTS-based algorithms by addressing their limitations through a unified, modular framework. This work not only advances the algorithmic capabilities of MCTS variants but also sets a foundation for future exploration in constructing general-purpose decision-making agents.