Prior works havepresented several means to combat this phenomenon in IL.

Prior works havepresented several means to combat this phenomenon in IL.

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.

Beyond existing meta-RL analyses, we provide upper bounds of the expected optimality gap over the task distribution.

Most reinforcement learning methods rely heavily on dense, well-normalized environmentrewards.

While knowntobesample efficient, these methods havefailed tofully leverage recent advances indeep learning, forcing the use of less efficient but more scalable model-free methods which try to learn the values directly.

While knowntobesample efficient, these methods havefailed tofully leverage recent advances indeep learning, forcing the use of less efficient but more scalable model-free methods which try to learn the values directly.