# Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings

arXiv: https://arxiv.org/abs/2011.12582 · HTML: https://mlsanity.com/paper/2011.12582/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2020.

## Built on by (most cited first, 18 of 18)

- [Imitating Human Behaviour with Diffusion Models](https://mlsanity.com/paper/2301.10677/) (2023)
- [JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2112.04907/) (2021)
- [Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning](https://mlsanity.com/paper/2209.08483/) (2022)
- [TiKick: Towards Playing Multi-agent Football Full Games from Single-agent Demonstrations](https://mlsanity.com/paper/2110.04507/) (2021)
- [Pretraining in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2211.03959/) (2022)
- [Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization](https://mlsanity.com/paper/2209.00347/) (2022)
- [Revisiting Discrete Soft Actor-Critic](https://mlsanity.com/paper/2209.10081/) (2022)
- [Affordable Generative Agents](https://mlsanity.com/paper/2402.02053/) (2024)
- [Boosting Offline Reinforcement Learning with Residual Generative Modeling](https://mlsanity.com/paper/2106.10411/) (2021)
- [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)
- [LLMs May Not Be Human-Level Players, But They Can Be Testers: Measuring Game Difficulty with LLM Agents](https://mlsanity.com/paper/2410.02829/) (2024)
- [ADAPTER-RL: Adaptation of Any Agent using Reinforcement Learning](https://mlsanity.com/paper/2311.11537/) (2023)
- [Accelerating Self-Imitation Learning from Demonstrations via Policy Constraints and Q-Ensemble](https://mlsanity.com/paper/2212.03562/) (2022)
- [Towards Controllable Agent in MOBA Games with Generative Modeling](https://mlsanity.com/paper/2112.08093/) (2021)
- [Guidance Design for Escape Flight Vehicle Using Evolution Strategy Enhanced Deep Reinforcement Learning](https://mlsanity.com/paper/2405.03711/) (2024)
- [Pareto-guided Pipeline for Distilling Featherweight AI Agents in Mobile MOBA Games](https://mlsanity.com/paper/2602.07521/) (2026)

## Abstract (arXiv)

We present JueWu-SL, the first supervised-learning-based artificial intelligence (AI) program that achieves human-level performance in playing multiplayer online battle arena (MOBA) games. Unlike prior attempts, we integrate the macro-strategy and the micromanagement of MOBA-game-playing into neural networks in a supervised and end-to-end manner. Tested on Honor of Kings, the most popular MOBA at present, our AI performs competitively at the level of High King players in standard 5v5 games.