# Hierarchical Reinforcement Learning for Multi-agent MOBA Game

arXiv: https://arxiv.org/abs/1901.08004 · HTML: https://mlsanity.com/paper/1901.08004/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2019.

## Built on by (most cited first, 4 of 4)

- [ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency](https://mlsanity.com/paper/2211.16068/) (2022)
- [Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2412.15517/) (2024)
- [Policy Diversity for Cooperative Agents](https://mlsanity.com/paper/2308.14308/) (2023)
- [Scaling Artificial Intelligence for Digital Wargaming in Support of Decision-Making](https://mlsanity.com/paper/2402.06075/) (2024)

## Abstract (arXiv)

Real Time Strategy (RTS) games require macro strategies as well as micro strategies to obtain satisfactory performance since it has large state space, action space, and hidden information. This paper presents a novel hierarchical reinforcement learning model for mastering Multiplayer Online Battle Arena (MOBA) games, a sub-genre of RTS games. The novelty of this work are: (1) proposing a hierarchical framework, where agents execute macro strategies by imitation learning and carry out micromanipulations through reinforcement learning, (2) developing a simple self-learning method to get better sample efficiency for training, and (3) designing a dense reward function for multi-agent cooperation in the absence of game engine or Application Programming Interface (API). Finally, various experiments have been performed to validate the superior performance of the proposed method over other state-of-the-art reinforcement learning algorithms. Agent successfully learns to combat and defeat bronze-level built-in AI with 100% win rate, and experiments show that our method can create a competitive multi-agent for a kind of mobile MOBA game it King of Glory in 5v5 mode.