Qatten: A General Framework for Cooperative Multiagent Reinforcement Learning

Understand

In many real-world tasks, multiple agents must learn to coordinate with each other given their private observations and limited communication ability.

• Deep multiagent reinforcement learning (Deep-MARL) algorithms have shown superior performance in such challenging settings.
• One representative class of work is multiagent value decomposition, which decomposes the global shared multiagent Q-value $Q_{tot}$ into individual Q-values $Q^{i}$ to guide individuals' behaviors, i.e.
• VDN imposing an additive formation and QMIX adopting a monotonic assumption using an implicit mixing method.