Can causal reasoning improve multi-agent RL?
A ROADMAP TOWARDS IMPROVING MULTI-AGENT REINFORCEMENT LEARNING WITH CAUSAL DISCOVERY AND INFERENCE
March 25, 2025
https://arxiv.org/pdf/2503.17803This paper explores using causal reasoning to improve multi-agent reinforcement learning (MARL). It proposes a framework called Causality-Driven Reinforcement Learning (CDRL) that learns a simplified causal model of the environment dynamics (how actions influence states and rewards) and then uses this model to filter out risky actions and guide agents towards better outcomes. Experiments showed mixed results: while the causal augmentation improved performance in some tasks, particularly for independent learners in less cooperative scenarios, it struggled when agents needed to heavily collaborate.
Key points for LLM-based multi-agent systems:
- Causal reasoning can potentially improve the efficacy, efficiency, and safety of MARL policies, especially in less cooperative scenarios or when safety is a primary concern.
- Learning and leveraging a causal model of the environment can provide valuable insights into how actions influence outcomes, which aligns with the goal of creating more interpretable and predictable LLM agents.
- Applying causal discovery to LLM-based agents raises challenges, including the computational complexity of causal discovery in high-dimensional spaces and the issue of validating the learned causal models.
- The paper highlights the potential for integrating causal reasoning with various MARL algorithms, even those using deep learning, offering avenues for enhancing current LLM-based multi-agent systems.
- The paper's focus on interventions and counterfactual reasoning, which are key concepts in causality, provides a solid theoretical foundation for building LLM agents capable of reasoning about the consequences of their actions.