mlsanityData to 7 Oct 2026

Mini Honor of Kings: A Lightweight Environment for Multi-Agent Reinforcement Learning

arXiv 2406.03978 ↗First posted 6 Jun 2024cs.MA cs.LGcited by 0

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Abstract (arXiv)

Games are widely used as research environments for multi-agent reinforcement learning (MARL), but they pose three significant challenges: limited customization, high computational demands, and oversimplification. To address these issues, we introduce the first publicly available map editor for the popular mobile game Honor of Kings and design a lightweight environment, Mini Honor of Kings (Mini HoK), for researchers to conduct experiments. Mini HoK is highly efficient, allowing experiments to be run on personal PCs or laptops while still presenting sufficient challenges for existing MARL algorithms. We have tested our environment on common MARL algorithms and demonstrated that these algorithms have yet to find optimal solutions within this environment. This facilitates the dissemination and advancement of MARL methods within the research community. Additionally, we hope that more researchers will leverage the Honor of Kings map editor to develop innovative and scientifically valuable new maps. Our code and user manual are available at: https://github.com/tencent-ailab/mini-hok.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 41 references that are in the map. First posted on arXiv 6 Jun 2024.

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23 of 41 references.

References · 23

  1. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,084
  2. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 903
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  8. 2019Google Research Football: A Novel Reinforcement Learning Environmentcited by 257
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  12. 2021Trust Region Policy Optimisation in Multi-Agent Reinforcement Learningcited by 180
11 more
  1. 2020Qatten: A General Framework for Cooperative Multiagent Reinforcement Learningcited by 81
  2. 2021Rethinking the Implementation Tricks and Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learningcited by 68
  3. 2021Cooperative Exploration for Multi-Agent Deep Reinforcement Learningcited by 57
  4. 2019DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learningcited by 54
  5. 2022Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learningcited by 27
  6. 2019Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advancescited by 23
  7. 2021Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignmentcited by 12
  8. 2020Which Heroes to Pick? Learning to Draft in MOBA Games with Neural Networks and Tree Searchcited by 10
  9. 2022CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement Learningcited by 7
  10. 2021KnowRU: Knowledge Reusing via Knowledge Distillation in Multi-agent Reinforcement Learningcited by 6
  11. 2022MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement Learningcited by 3

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