# Which Heroes to Pick? Learning to Draft in MOBA Games with Neural Networks and Tree Search

arXiv: https://arxiv.org/abs/2012.10171 · HTML: https://mlsanity.com/paper/2012.10171/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2020.

## Built on by (most cited first, 10 of 10)

- [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)
- [A Survey on Self-play Methods in Reinforcement Learning](https://mlsanity.com/paper/2408.01072/) (2024)
- [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)
- [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)
- [CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2203.08412/) (2022)
- [Mini Honor of Kings: A Lightweight Environment for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2406.03978/) (2024)

## Abstract (arXiv)

Hero drafting is essential in MOBA game playing as it builds the team of each side and directly affects the match outcome. State-of-the-art drafting methods fail to consider: 1) drafting efficiency when the hero pool is expanded; 2) the multi-round nature of a MOBA 5v5 match series, i.e., two teams play best-of-N and the same hero is only allowed to be drafted once throughout the series. In this paper, we formulate the drafting process as a multi-round combinatorial game and propose a novel drafting algorithm based on neural networks and Monte-Carlo tree search, named JueWuDraft. Specifically, we design a long-term value estimation mechanism to handle the best-of-N drafting case. Taking Honor of Kings, one of the most popular MOBA games at present, as a running case, we demonstrate the practicality and effectiveness of JueWuDraft when compared to state-of-the-art drafting methods.