Model-free deepreinforcement learning (RL)algorithms havebeenwidely used for a range of complex control tasks.

Existing methods for agent modelling commonly assume knowledge of the local observations and chosen actions of the modelled agents during execution. To eliminate this assumption, we extract representations from thelocalinformation ofthecontrolled agent using encoderdecoderarchitectures.

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.