  Deducing the contribution of each agent and assigning the corresponding
reward to them is a crucial problem in cooperative Multi-Agent Reinforcement
Learning (MARL). Previous studies try to resolve the issue through designing an
intrinsic reward function, but the intrinsic reward is simply combined with the
environment reward by summation in these studies, which makes the performance
of their MARL framework unsatisfactory. We propose a novel method named
Attention Individual Intrinsic Reward Mixing Network (AIIR-MIX) in MARL, and
the contributions of AIIR-MIX are listed as follows:(a) we construct a novel
intrinsic reward network based on the attention mechanism to make teamwork more
effective. (b) we propose a Mixing network that is able to combine intrinsic
and extrinsic rewards non-linearly and dynamically in response to changing
conditions of the environment. We compare AIIR-MIX with many State-Of-The-Art
(SOTA) MARL methods on battle games in StarCraft II. And the results
demonstrate that AIIR-MIX performs admirably and can defeat the current
advanced methods on average test win rate. To validate the effectiveness of
AIIR-MIX, we conduct additional ablation studies. The results show that
AIIR-MIX can dynamically assign each agent a real-time intrinsic reward in
accordance with their actual contribution.
