Towards Controllable Agent in MOBA Games with Generative Modeling

• URL: http://arxiv.org/abs/2112.08093v1
• Date: Wed, 15 Dec 2021 13:09:22 GMT
• Title: Towards Controllable Agent in MOBA Games with Generative Modeling
• Authors: Shubao Zhang
• Abstract summary: We propose novel methods to develop action controllable agent that behaves like a human. We devise a deep latent alignment neural network model for training agent, and a corresponding sampling algorithm for controlling an agent's action. Both simulated and online experiments in the game Honor of Kings demonstrate the efficacy of the proposed methods.
• Score: 0.45687771576879593
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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