Factorized Q-Learning for Large-Scale Multi-Agent Systems

Understand

Deep Q-learning has achieved significant success in single-agent decision making tasks.

• However, it is challenging to extend Q-learning to large-scale multi-agent scenarios, due to the explosion of action space resulting from the complex dynamics between the environment and the agents.
• In this paper, we propose to make the computation of multi-agent Q-learning tractable by treating the Q-function (w.r.t.
• state and joint-action) as a high-order high-dimensional tensor and then approximate it with factorized pairwise interactions.