  Cooperative multi-agent tasks require agents to deduce their own
contributions with shared global rewards, known as the challenge of credit
assignment. General methods for policy based multi-agent reinforcement learning
to solve the challenge introduce differentiate value functions or advantage
functions for individual agents. In multi-agent system, polices of different
agents need to be evaluated jointly. In order to update polices synchronously,
such value functions or advantage functions also need synchronous evaluation.
However, in current methods, value functions or advantage functions use
counter-factual joint actions which are evaluated asynchronously, thus suffer
from natural estimation bias. In this work, we propose the approximatively
synchronous advantage estimation. We first derive the marginal advantage
function, an expansion from single-agent advantage function to multi-agent
system. Further more, we introduce a policy approximation for synchronous
advantage estimation, and break down the multi-agent policy optimization
problem into multiple sub-problems of single-agent policy optimization. Our
method is compared with baseline algorithms on StarCraft multi-agent
challenges, and shows the best performance on most of the tasks.
