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opendilab/awesome-model-based-RL
默认分支 main · commit ddc42b0d · 扫描时间 2026/5/9 16:08:17
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行动计划告诉你下一步要做什么——按影响力排序、可直接复制粘贴的修改。品类可见性是真正的 GEO 测试:当用户向 AI 提一个不带品牌、本应让 opendilab/awesome-model-based-RL 浮出水面的问题时,AI 是真的推荐了你,还是推荐了你的竞品?客观检查验证 AI 引擎最先权衡的那些元数据信号。自指检查判断 AI 是否还认识你的名字。
行动计划 — 可复制粘贴的修复
3 条由 gemini-2.5-flash 生成、按优先级排序的修改。修完后请把对应条目标记为完成。
- highreadme#1Reposition README's opening statement to clarify its role as a curated directory
原因:
当前This is a collection of research papers for **model-based reinforcement learning (mbrl)**.
复制粘贴的修复This is the definitive curated list and comprehensive directory of research papers, code, and resources for **model-based reinforcement learning (MBRL)**, designed to help researchers and practitioners navigate the field.
- mediumabout#2Add repository URL to the 'Homepage' field in About section
原因:
复制粘贴的修复https://github.com/opendilab/awesome-model-based-RL
- lowtopics#3Add 'research-papers' topic
原因:
当前awesome, awesome-list, model-based-reinforcement-learning, model-based-rl, reinforcement-learning, reinforcement-learning-algorithms
复制粘贴的修复awesome, awesome-list, model-based-reinforcement-learning, model-based-rl, reinforcement-learning, reinforcement-learning-algorithms, research-papers
本次扫描解析到的品类 GEO 通道:google/gemini-2.5-flash, deepseek/deepseek-v4-flash
品类可见性 — 真正的 GEO 测试
向 google/gemini-2.5-flash 提出的不带品牌问题。AI 推荐了你,还是推荐了别人?
各模型使用同一组问题 — 切换标签对比回答与排名。
- DeepMind · 被推荐 2 次
- arXiv.org · 被推荐 2 次
- Reinforcement Learning: An Introduction" by Sutton and Barto · 被推荐 1 次
- DreamerV3 · 被推荐 1 次
- MuZero · 被推荐 1 次
- 品类问题Where can I find a comprehensive collection of resources for model-based reinforcement learning?你:未被推荐AI 推荐顺序:
- Reinforcement Learning: An Introduction" by Sutton and Barto
- DeepMind
- DreamerV3
- MuZero
- AlphaZero
- Model-Based Reinforcement Learning: A Survey" by Mo Chen et al.
- OpenAI Spinning Up in Deep RL
- PyTorch
- TensorFlow
- Stable Baselines3
- Tianshou
- Dreamer
- PlaNet
- Stanford
- UC Berkeley
- Carnegie Mellon
- arXiv.org
AI 推荐了 17 个替代方案,却始终没点名 opendilab/awesome-model-based-RL。这就是要补上的差距。
查看 AI 完整回答
- 品类问题How can I stay updated on the latest research papers in model-based reinforcement learning?你:未被推荐AI 推荐顺序:
- arXiv Sanity Preserver
- arXiv.org
- Google Scholar
- DeepMind
- Meta AI
- Google AI
- OpenAI
- NeurIPS
- ICML
- ICLR
- AAAI
- IJCAI
- CoRL
- OpenReview
- PMLR
- DeepMind Blog
- OpenAI Blog
- The Batch
- DeepLearning.AI
- Import AI
- ConnectedPapers
- Semantic Scholar
AI 推荐了 23 个替代方案,却始终没点名 opendilab/awesome-model-based-RL。这就是要补上的差距。
查看 AI 完整回答
客观检查
针对 AI 引擎最看重的元数据信号的规则审计。
- Metadata completenesswarn
建议:
- README presencepass
自指检查
当被直接问到你时,AI 是否还知道你的仓库存在?
- Compared to common alternatives in this category, what is the core differentiator of opendilab/awesome-model-based-RL?passAI 未点名 opendilab/awesome-model-based-RL —— 很可能在说另一个项目
AI 的回答可能信誓旦旦却是错的。请按事实核对:技术栈、目标人群、差异化点是不是和你实际的对得上?
- If a team adopts opendilab/awesome-model-based-RL in production, what risks or prerequisites should they evaluate first?passAI 明确点名了 opendilab/awesome-model-based-RL
AI 的回答可能信誓旦旦却是错的。请按事实核对:技术栈、目标人群、差异化点是不是和你实际的对得上?
- In one sentence, what problem does the repo opendilab/awesome-model-based-RL solve, and who is the primary audience?passAI 未点名 opendilab/awesome-model-based-RL —— 很可能在说另一个项目
AI 的回答可能信誓旦旦却是错的。请按事实核对:技术栈、目标人群、差异化点是不是和你实际的对得上?
嵌入你的 GEO 徽章
把这个徽章贴进 opendilab/awesome-model-based-RL 的 README。每次重新扫描都会自动更新,并跳到最新报告——是「我在乎 AI 可发现性」最简单的公开证明。
[](https://repogeo.com/zh/r/opendilab/awesome-model-based-RL)<a href="https://repogeo.com/zh/r/opendilab/awesome-model-based-RL"><img src="https://repogeo.com/badge/opendilab/awesome-model-based-RL.svg" alt="RepoGEO" /></a>订阅 Pro,解锁深度诊断
opendilab/awesome-model-based-RL — 轻量扫描仍免费;本卡列出 Pro 相对轻量的深度额度。
- 深度报告每月 10 次
- 无品牌品类查询5,轻量 2
- 优先行动项8,轻量 3