# NRN Agents Value to Studios ## Enhance Player Experience NRN Agents can be used in many ways to add value throughout a studio's lifecycle. **Indie Studios** Developers can swiftly prototype and scale human-like AI agents for multiplayer and PvP games. Studios can also tap into NRN's Trainer Platform to efficiently crowdsource agents from skilled players. This significantly boosts matchmaking liquidity, enhancing player experience and retention. **Established Studios** By fully integrating an imitation learning loop into games, where players can train their own agents to mimic their playstyle, NRN Agents empower 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. ## Scale Infrastructure Efficiently NRN Agents help studios scale infrastructure efficiently. The NRN platform leverages a proprietary machine learning model, which minimizes data requirements, computational demands, server expenditures and accelerates the training process. Traditional platforms use ML models that require growing data sets that inflate storage and compute costs. NRN models are able to retain previously-learned knowledge even when there is introduction of new data. Data used to train the model can be discarded after each training iteration, curbing the ever expanding data needs and minimizing computation demands. ## Improve Monetization Potential Game monetization often faces limitations due to player availability. By addressing player liquidity challenges, studios can boost the number of matches and in-game interactions. Integrating human-like AI agents offers a solution, as these agents are available 24/7, 365 days a year. These AI agents can simultaneously participate in multiple matches, game instances, or tournaments—a concept known as Parallel Play. This capability significantly expands the potential for player engagement and monetization. --- # Agent Instructions: 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: ``` GET https://whitepaper.nrnagents.ai/nrn-b2b/nrn-agents-value-to-studios.md?ask= ``` The question should be specific, self-contained, and written in natural language. 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.