# Hierarchical Macro Strategy Model for MOBA Game AI

arXiv: https://arxiv.org/abs/1812.07887 · HTML: https://mlsanity.com/paper/1812.07887/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2018.

## Built on by (most cited first, 12 of 12)

- [Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning](https://mlsanity.com/paper/2209.08483/) (2022)
- [LINDA: Multi-Agent Local Information Decomposition for Awareness of Teammates](https://mlsanity.com/paper/2109.12508/) (2021)
- [A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games](https://mlsanity.com/paper/2509.03682/) (2025)
- [Learning Diverse Policies in MOBA Games via Macro-Goals](https://mlsanity.com/paper/2110.14221/) (2021)
- [Towards Effective and Interpretable Human-Agent Collaboration in MOBA Games: A Communication Perspective](https://mlsanity.com/paper/2304.11632/) (2023)
- [Enhancing Human Experience in Human-Agent Collaboration: A Human-Centered Modeling Approach Based on Positive Human Gain](https://mlsanity.com/paper/2401.16444/) (2024)
- [Enabling All In-Edge Deep Learning: A Literature Review](https://mlsanity.com/paper/2204.03326/) (2022)
- [Guiding Multi-agent Multi-task Reinforcement Learning by a Hierarchical Framework with Logical Reward Shaping](https://mlsanity.com/paper/2411.01184/) (2024)
- [A Survey of Decision Making in Adversarial Games](https://mlsanity.com/paper/2207.07971/) (2022)
- [JiangJun: Mastering Xiangqi by Tackling Non-Transitivity in Two-Player Zero-Sum Games](https://mlsanity.com/paper/2308.04719/) (2023)
- [Policy Diversity for Cooperative Agents](https://mlsanity.com/paper/2308.14308/) (2023)
- [Causal Mean Field Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2502.14200/) (2025)

## Abstract (arXiv)

The next challenge of game AI lies in Real Time Strategy (RTS) games. RTS games provide partially observable gaming environments, where agents interact with one another in an action space much larger than that of GO. Mastering RTS games requires both strong macro strategies and delicate micro level execution. Recently, great progress has been made in micro level execution, while complete solutions for macro strategies are still lacking. In this paper, we propose a novel learning-based Hierarchical Macro Strategy model for mastering MOBA games, a sub-genre of RTS games. Trained by the Hierarchical Macro Strategy model, agents explicitly make macro strategy decisions and further guide their micro level execution. Moreover, each of the agents makes independent strategy decisions, while simultaneously communicating with the allies through leveraging a novel imitated cross-agent communication mechanism. We perform comprehensive evaluations on a popular 5v5 Multiplayer Online Battle Arena (MOBA) game. Our 5-AI team achieves a 48% winning rate against human player teams which are ranked top 1% in the player ranking system.