Can causal modeling improve scalable multi-agent RL?
Causal Mean Field Multi-Agent Reinforcement Learning
This paper introduces Causal Mean Field Q-learning (CMFQ), a new algorithm designed to improve the scalability and robustness of multi-agent reinforcement learning (MARL) in environments with a large number of agents. CMFQ addresses the "curse of dimensionality" by simplifying interactions between numerous agents using mean field theory and tackles the non-stationarity problem (where other agents' constantly changing policies make learning difficult) by prioritizing crucial pairwise interactions identified through causal inference. This causal approach allows the agent to focus on interactions that truly matter, leading to more efficient learning and better overall performance.
For LLM-based multi-agent systems, CMFQ offers a potential pathway to more scalable and robust applications. By identifying and focusing on causally relevant interactions, CMFQ could improve the efficiency and effectiveness of LLMs working together in complex, multi-agent scenarios. This approach could be particularly relevant for applications involving a significant number of interacting LLMs, where traditional MARL methods struggle with scalability. The causality-aware representation of other agents also makes the system more robust to changes in the number of agents during execution, which further enhances its scalability.