Towards Controllable Agent in MOBA Games with Generative Modeling [article]

Shubao Zhang
2021 arXiv   pre-print
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
more » ... ignment model. Both simulated and online experiments in the game Honor of Kings demonstrate the efficacy of the proposed methods.
arXiv:2112.08093v1 fatcat:hvykvhyyhjhdzcb6hwbcsto4sa