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Towards Controllable Agent in MOBA Games with Generative Modeling

arXiv 2112.08093 ↗First posted 15 Dec 2021cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 39 references that are in the map. First posted on arXiv 15 Dec 2021.

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27 of 39 references.

References · 27

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15 more
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  15. 2019Why Build an Assistant in Minecraft?cited by 14

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