How can masked autoencoders improve multi-agent RL generalization?
MA2RL: Masked Autoencoders for Generalizable Multi-Agent Reinforcement Learning
February 25, 2025
https://arxiv.org/pdf/2502.17046This paper introduces MA2RL, a new framework for training AI agents that can work together effectively, even when they can't see everything happening around them (like in many real-world situations). It borrows the idea of "masked autoencoders" (MAE) from image and language processing, where a model learns to fill in missing information. In MA2RL, each agent treats its limited view as a "mask" over the complete world state and learns to infer the missing information about other agents and the environment. This shared understanding helps them coordinate better.
Key points for LLM-based multi-agent systems:
- Partial Observability: MA2RL tackles the crucial challenge of limited information, common in real-world multi-agent scenarios and relevant to LLMs working with incomplete knowledge.
- Generalization: MA2RL improves generalization across tasks with different numbers of agents or actions, suggesting potential for adaptable LLM-based agents.
- Entity-Level Perspective: MA2RL works at the level of individual entities (agents, objects), potentially facilitating modular design and easier integration with LLMs representing entities.
- Skill Learning: MA2RL incorporates the concept of "skills," which can be thought of as high-level actions or strategies, allowing LLM-based agents to reason at different levels of abstraction.
- Implicit World Modeling: The MAE component implicitly builds a model of the world state, which is relevant to how LLMs might build representations of situations.