Causal Mean Field Multi-Agent Reinforcement Learning
This addresses scalability issues for researchers and practitioners in multi-agent systems, though it appears incremental as it builds on existing mean-field reinforcement learning.
The paper tackles the scalability challenge in multi-agent reinforcement learning by proposing causal mean-field Q-learning (CMFQ), which integrates causality into mean-field reinforcement learning to identify essential interactions in nonstationary environments, resulting in excellent scalability performance in training and testing with large numbers of agents.
Scalability remains a challenge in multi-agent reinforcement learning and is currently under active research. A framework named mean-field reinforcement learning (MFRL) could alleviate the scalability problem by employing the Mean Field Theory to turn a many-agent problem into a two-agent problem. However, this framework lacks the ability to identify essential interactions under nonstationary environments. Causality contains relatively invariant mechanisms behind interactions, though environments are nonstationary. Therefore, we propose an algorithm called causal mean-field Q-learning (CMFQ) to address the scalability problem. CMFQ is ever more robust toward the change of the number of agents though inheriting the compressed representation of MFRL's action-state space. Firstly, we model the causality behind the decision-making process of MFRL into a structural causal model (SCM). Then the essential degree of each interaction is quantified via intervening on the SCM. Furthermore, we design the causality-aware compact representation for behavioral information of agents as the weighted sum of all behavioral information according to their causal effects. We test CMFQ in a mixed cooperative-competitive game and a cooperative game. The result shows that our method has excellent scalability performance in both training in environments containing a large number of agents and testing in environments containing much more agents.