Honor Of Kings Arena: An Environment For Generalization In Competitive Reinforcement Learning

Abstract

This paper introduces Honor of Kings Arena, a reinforcement learning (RL) environment based on Honor of Kings, one of the world's most popular games at present. Compared to other environments studied in most previous work, ours presents new generalization challenges for competitive reinforcement learning. It is a multi-agent problem with one agent competing against its opponent; and it requires the generalization ability as it has diverse targets to control and diverse opponents to compete with. We describe the observation, action, and reward specifications for the Honor of Kings domain and provide an open-source Python-based interface for communicating with the game engine. We provide twenty target heroes with a variety of tasks in Honor of Kings Arena and present initial baseline results for RL-based methods with feasible computing resources. Finally, we showcase the generalization challenges imposed by Honor of Kings Arena and possible remedies to the challenges. All of the software

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keywei2022honor

Related papers

• Mini Honor Of Kings: A Lightweight Environment For Multi-agent Reinforcement Learning (2024)3.42
• The AI Arena: A Framework For Distributed Multi-agent Reinforcement Learning (2021)0.00
• On Generalization Across Environments In Multi-objective Reinforcement Learning (2025)0.00
• Hokoff: Real Game Dataset From Honor Of Kings And Its Offline Reinforcement Learning Benchmarks (2024)0.00
• Options As Responses: Grounding Behavioural Hierarchies In Multi-agent RL (2019)0.00
• Eden: A Unified Environment Framework For Booming Reinforcement Learning Algorithms (2021)0.00
• Fightladder: A Benchmark For Competitive Multi-agent Reinforcement Learning (2024)0.00
• Simplified Action Decoder For Deep Multi-agent Reinforcement Learning (2019)4.03