用强化学习+大模型自动修游戏，让设计迭代更智能
Fly, Fail, Fix: Iterative Game Repair with Reinforcement Learning and Large Multimodal Models
- RL玩家试玩生成行为数据，大模型据此修改游戏配置
- 通过多轮迭代，游戏机制逐步逼近预设玩法目标
- 适合游戏设计师和AI辅助开发团队快速优化关卡
游戏设计依赖于理解静态规则与内容如何转化为动态玩家行为，而当前仅分析代码或资源的生成系统难以捕捉这一过程。我们提出一种自动化设计迭代框架，将强化学习（RL）代理与大型多模态模型（LMM）结合：RL代理玩玩游戏，产生数值指标和近期视频帧的图像摘要；LMM设计者接收玩法目标与当前游戏配置，分析行为轨迹，修改配置以引导未来行为向目标靠拢。实验表明，LMM能基于RL生成的行为轨迹推理并逐步优化游戏机制，为AI辅助游戏设计提供了可扩展、实用的新工具。
原文摘要 · Abstract (English)
Game design hinges on understanding how static rules and content translate into dynamic player behavior - something modern generative systems that inspect only a game's code or assets struggle to capture. We present an automated design iteration framework that closes this gap by pairing a reinforcement learning (RL) agent, which playtests the game, with a large multimodal model (LMM), which revises the game based on what the agent does. In each loop the RL player completes several episodes, producing (i) numerical play metrics and/or (ii) a compact image strip summarising recent video frames. The LMM designer receives a gameplay goal and the current game configuration, analyses the play traces, and edits the configuration to steer future behaviour toward the goal. We demonstrate results that LMMs can reason over behavioral traces supplied by RL agents to iteratively refine game mechanics, pointing toward practical, scalable tools for AI-assisted game design.
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