> For the complete documentation index, see [llms.txt](https://whitepaper.nrnagents.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://whitepaper.nrnagents.ai/nrn-b2b/nrn-agents-value-to-studios/white-label-ai-partner.md). # White Label AI Partner Beyond player liquidity, studios can **integrate NRN Agents to create new AI game experiences.** By fully integrating an imitation learning loop into games, where players can train their own agents to mimic their playstyle, NRN empowers studios to create novel gameplay experiences. These agents are high-fidelity replicas of their human players' skills. This capability applies to both single-player and multiplayer experiences, and can be released as standalone games or AI-enhanced game modes. With human players' skills encapsulated in AI agents, players can extend their presence across multiple environments within the same game or even different game modes simultaneously. This dramatically increases the monetization potential for game studios. ## **Case Studies** **AI Arena** NRN Agents enable players in AI Arena to train AI characters and compete in a PvP fighting game. In this setting, the AI adopts unique strategies and characteristics from its human trainer through a process known as imitation learning. AI Arena merges the traditional aspects of a platform-fighter with the dynamism of AI, providing players with a unique and unparalleled gaming experience. For more information please visit [AI Arena Documentation Site](https://docs.aiarena.io). **Other Case Studies Coming Soon** **Types of projects we are currently working on** * AAA studio - Top down shooter game * Established web2 studio - social casino game --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://whitepaper.nrnagents.ai/nrn-b2b/nrn-agents-value-to-studios/white-label-ai-partner.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.