Towards Understanding Cooperative Multi-Agent Q-Learning with Value FactorizationDownload PDF

21 May 2021, 20:46 (modified: 22 Jan 2022, 02:47)NeurIPS 2021 PosterReaders: Everyone
Keywords: Multi-Agent Reinforcement Learning, Reinforcement Learning Theory, Value Factorization, Fitted Q-Iteration
TL;DR: We provide a formal theoretical analysis to investigate the algorithmic properties of linear value factorizaiton and IGM value factorization in cooperative multi-agent Q-learning.
Abstract: Value factorization is a popular and promising approach to scaling up multi-agent reinforcement learning in cooperative settings, which balances the learning scalability and the representational capacity of value functions. However, the theoretical understanding of such methods is limited. In this paper, we formalize a multi-agent fitted Q-iteration framework for analyzing factorized multi-agent Q-learning. Based on this framework, we investigate linear value factorization and reveal that multi-agent Q-learning with this simple decomposition implicitly realizes a powerful counterfactual credit assignment, but may not converge in some settings. Through further analysis, we find that on-policy training or richer joint value function classes can improve its local or global convergence properties, respectively. Finally, to support our theoretical implications in practical realization, we conduct an empirical analysis of state-of-the-art deep multi-agent Q-learning algorithms on didactic examples and a broad set of StarCraft II unit micromanagement tasks.
Supplementary Material: pdf
Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.
Code: zip
19 Replies