  We propose novel methods to develop action controllable agent that behaves
like a human and has the ability to align with human players in Multiplayer
Online Battle Arena (MOBA) games. By modeling the control problem as an action
generation process, we devise a deep latent alignment neural network model for
training agent, and a corresponding sampling algorithm for controlling an
agent's action. Particularly, we propose deterministic and stochastic attention
implementations of the core latent alignment model. Both simulated and online
experiments in the game Honor of Kings demonstrate the efficacy of the proposed
methods.
