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.
翻译:我们提出了开发可操作控制剂的新颖方法,该物剂的行为举止像人类,并且有能力在多人在线战斗竞技场(MOBA)游戏中与人玩家保持一致。通过将控制问题模拟为行动生成过程,我们为培训剂设计了一个深潜的匹配神经网络模型,以及一个相应的样本算法,用于控制代理人的行动。特别是,我们提出了核心潜在匹配模型的确定性和随机关注实施。在国王荣誉游戏中的模拟和在线实验都展示了拟议方法的功效。