# Permanent Player Liquidity as a Service

## NRN Agents solve the problem of **player liquidity**

Player liquidity refers to the availability of active players in a game. High liquidity ensures quick matchmaking and diverse opponents, while low liquidity leads to long wait times and repetitive matches, diminishing the player experience.

## **Why is player liquidity a problem?**

**Matchmaking Times | Quality**: Good player liquidity ensures that players are matched with opponents or teammates of similar skill levels quickly, which is crucial for maintaining a balanced and enjoyable gaming experience.

**Player Retention**: Low liquidity can lead to frustration due to long wait times or poor match quality, causing players to leave the game, further exacerbating the problem. This can create a negative feedback loop where a declining player base leads to even lower liquidity.

**Game Lifespan**: High player liquidity contributes to the longevity of a game. A vibrant, active player base helps sustain the community, attract new players, and keep the game alive over time.

**Player liquidity is particularly problematic for indie games:** Player liquidity is a critical challenge for indie games, leading to early attrition, a vicious cycle of declining engagement, short lifespans, and difficulty gaining traction in an ever more competitive market.

## **How do NRN Agents solve this problem?**

Upon integrating the NRN Agents SDK, developers can swiftly prototype and scale human-like AI agents for their games. Studios can also tap into NRN'S Trainer Platform to efficiently crowdsource agents from skilled players. These agents simulate an active player base, allowing players to find matches even when human players are scarce.  Studios utilize NRN Agents to populate their games with human-like AI bots, significantly enhancing player liquidity.

## **Why is NRN better than the traditional approach?**

**Resource limitation** - NRN Agents significantly reduce the costs and complexity of bot development. Instead of coding bots from scratch, developers can create and train them by playing the game. Moreover, studios can directly leverage NRN's Trainer Platform, effectively outsourcing bot training to players. For indie studios and smaller developer teams with limited resources, this offers an affordable and scalable way to create a player experience historically limited to AAA studios with massive human and financial resources.

**Bot effectiveness** - Traditional AI bots are predictable and boring, often fall short in providing engaging gameplay. NRN's player-trained AI models offer more human-like behavior, addressing player liquidity issues more effectively. These models can be integrated into a game's skill-matching system, ensuring balanced gameplay across different skill levels. 


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