As aresult, full MOBAgames without restrictions are farfrom being mastered by any existing AI system. In this paper, we propose a MOBA AIlearning paradigm that methodologically enables playing full MOBAgames withdeepreinforcementlearning.Specifically,wedevelopacombinationofnovel and existing learning techniques, including curriculum self-play learning, policy distillation, off-policy adaption, multi-head value estimation, and Monte-Carlo tree-search, intraining andplaying alargepoolofheroes,meanwhile addressing thescalabilityissueskillfully.

It communicates among agents within the system to collaboratively solve tasks, under the premise of shared information. However, when agents' collaborations are leveraged to perform multi-person tasks, a new challenge arisesduetoinformation asymmetry,sinceeachagentcanonlyaccess theinformationofitshumanuser.