How to use data better in offline MARL?
Putting Data at the Centre of Offline Multi-Agent Reinforcement Learning
September 19, 2024
https://arxiv.org/pdf/2409.12001-
Main Topic: This paper argues that research in Offline Multi-agent Reinforcement Learning (MARL), while focused on algorithm development, overlooks the crucial aspect of data. It highlights how variations in dataset characteristics like average return, spread, distribution, and state-action coverage significantly impact algorithm performance, often more than algorithmic changes themselves.
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Key Points Relevant to LLM-based Multi-agent Systems:
- Dataset Awareness is Crucial: LLMs, being data-driven, are highly susceptible to dataset biases and limitations. This paper's findings directly apply to LLM-based multi-agent systems, emphasizing the need for careful data analysis and understanding its influence on agent behavior.
- State-Action Coverage Matters: The paper introduces "Joint-SACo," a metric measuring the diversity of state-action pairs in a dataset. This is particularly relevant to LLM agents, as limited Joint-SACo can lead to less robust and flexible behavior in novel situations.
- Standardized Datasets are Needed: The lack of standardized datasets in Offline MARL makes it difficult to objectively compare algorithms and hinders progress. This resonates with the LLM domain, where benchmark datasets are essential for evaluating and comparing different LLM architectures and training approaches for multi-agent scenarios.
- Data Manipulation Tools are Important: The paper introduces tools for analyzing, subsampling, and combining datasets. These are valuable for LLM researchers too, as they allow for creating diverse and tailored datasets to study specific LLM agent behaviors and challenges in controlled environments.