mlsanityData to 14 Oct 2026

RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games

arXiv 2307.01676 ↗First posted 4 Jul 2023cs.AIcited by 7

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Abstract (arXiv)

The balance of game content significantly impacts the gaming experience. Unbalanced game content diminishes engagement or increases frustration because of repetitive failure. Although game designers intend to adjust the difficulty of game content, this is a repetitive, labor-intensive, and challenging process, especially for commercial-level games with extensive content. To address this issue, the game research community has explored automated game balancing using artificial intelligence (AI) techniques. However, previous studies have focused on limited game content and did not consider the importance of the generalization ability of playtesting agents when encountering content changes. In this study, we propose RaidEnv, a new game simulator that includes diverse and customizable content for the boss raid scenario in MMORPG games. Additionally, we design two benchmarks for the boss raid scenario that can aid in the practical application of game AI. These benchmarks address two open problems in automatic content balancing, and we introduce two evaluation metrics to provide guidance for AI in automatic content balancing. This novel game research platform expands the frontiers of automatic game balancing problems and offers a framework within a realistic game production pipeline.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 35 of its 69 references that are in the map. First posted on arXiv 4 Jul 2023.

Builds on

35 of 69 references.

References · 35

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23 more
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Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 5.

7 papers.

  1. 2025HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agentscited by 6
  2. 2024ChatPCG: Large Language Model-Driven Reward Design for Procedural Content Generationcited by 5
  3. 2025IPCGRL: Language-Instructed Reinforcement Learning for Procedural Level Generationcited by 4
  4. 2024Return-Aligned Decision Transformercited by 3
  5. 2025PCGRLLM: Large Language Model-Driven Reward Design for Procedural Content Generation Reinforcement Learningcited by 3
  6. 2025Human-Aligned Procedural Level Generation Reinforcement Learning via Text-Level-Sketch Shared Representationcited by 2
  7. 2025UBCL: A Reinforcement Learning Framework for Controllable and Diverse Player Behaviorscited by 0