  Multi-agent reinforcement learning shines as the pinnacle of multi-agent
systems, conquering intricate real-world challenges, fostering collaboration
and coordination among agents, and unleashing the potential for intelligent
decision-making across domains. However, training a multi-agent reinforcement
learning network is a formidable endeavor, demanding substantial computational
resources to interact with diverse environmental variables, extract state
representations, and acquire decision-making knowledge. The recent
breakthroughs in large-scale pre-trained models ignite our curiosity: Can we
uncover shared knowledge in multi-agent reinforcement learning and leverage
pre-trained models to expedite training for future tasks? Addressing this
issue, we present an innovative multi-task learning approach that aims to
extract and harness common decision-making knowledge, like cooperation and
competition, across different tasks. Our approach involves concurrent training
of multiple multi-agent tasks, with each task employing independent front-end
perception layers while sharing back-end decision-making layers. This effective
decoupling of state representation extraction from decision-making allows for
more efficient training and better transferability. To evaluate the efficacy of
our proposed approach, we conduct comprehensive experiments in two distinct
environments: the StarCraft Multi-agent Challenge (SMAC) and the Google
Research Football (GRF) environments. The experimental results unequivocally
demonstrate the smooth transferability of the shared decision-making network to
other tasks, thereby significantly reducing training costs and improving final
performance. Furthermore, visualizations authenticate the presence of general
multi-agent decision-making knowledge within the shared network layers, further
validating the effectiveness of our approach.
