Data Collection via Agent Play in AR: Reward Mechanisms and UGC Compensation in a Dynamic Ecosystem
As we continue to shape our toolkits with the help of reinforcement learning (RL) agents, we're designing not just an immersive game, but an entire ecosystem where rewards, achievements, and user-generated content (UGC) are intrinsically linked. With real-world assets (RWAs) serving as the primary motivator for players and flexible compensation models for UGC creators, our goal is to create a system that not only drives engagement but also ensures fairness and long-term satisfaction for all participants.
In our AR ecosystem, the ultimate driver of player engagement will be real-world assets (RWAs). While virtual rewards such as gameplay enhancements or feature unlocks play an important role, RWAs will serve as the long-term achievement that players strive toward. The RL agents will help fine-tune the process by simulating different reward structures and understanding how players respond to the balance between short-term virtual rewards and long-term RWAs.
To ensure sustainability, our developers will substantiate the cost of each RWA, ensuring that the system remains scalable. The RL agents will test how different pathways toward RWAs, including a combination of virtual rewards and challenge completion, impact player motivation.
The RL agents will explore various reward structures, assessing how players respond to frequent but smaller rewards versus larger, milestone-based achievements. By simulating diverse player behaviors, agents can help identify the right frequency and magnitude for rewards, optimizing the balance between maintaining player interest and providing meaningful incentives. This balance is critical in a system where RWAs are the ultimate reward, ensuring that players remain engaged over long periods while working toward these valuable assets.
The reward system will be tightly linked to the completion of specific sets of challenges. RL agents will be tasked with simulating how players respond to different types of challenges and how the reward structure can be optimized to maintain engagement. The agents will also help determine which behaviors—whether exploration, puzzle-solving, or social interactions—should be most rewarded. The ultimate goal is to keep the reward structure engaging, ensuring that players feel motivated to complete the most challenging aspects of the game.
Players' reward preferences may shift over time, especially as they explore different playstyles. RL agents will simulate how these preferences evolve and ensure that the system rewards players more heavily as they complete challenges while adopting new strategies or styles of play. This dynamic reward system will encourage players to diversify their approach, keeping the experience fresh and rewarding.
For creators of UGC-turned-NFTs, RL agents will explore different compensation models, with a primary focus on usage-based compensation. As players use or interact with UGC in various capacities, the creators will be compensated based on how often their content is employed. At the time of NFT design, creators will be given options for how they wish to be compensated, allowing flexibility in how they engage with the marketplace.
RL agents will simulate various scenarios to determine how to balance rewards for creators. For instance, if a popular piece of UGC is used frequently, the compensation structure will need to reflect that demand without overwhelming the system. RL agents will also help fine-tune how creators are rewarded based on the impact their UGC has on gameplay, allowing us to establish a fair and scalable compensation model.
RL agents will also explore how to adjust compensation based on the quality and complexity of UGC. High-quality content, such as intricate puzzles or immersive environments, will be compensated more generously, ensuring that creators are incentivized to produce content that enhances the player experience. By analyzing how users engage with different types of UGC, RL agents can help us understand the value that these contributions bring to the overall ecosystem.
Even when users modify existing UGC-turned-NFTs, the original creators will still receive a share of the compensation. RL agents will help determine how the degree of modification impacts compensation, ensuring that the original creators continue to benefit from their work. Importantly, the original compensation arrangement, embedded in the metadata of the NFT, will not be altered. However, users who customize or modify the content can impose an additional fee, offering another layer of customization and reward potential within the ecosystem.
As we approach our pre-token launch, RL agents play a vital role in ensuring that both players and UGC creators are treated fairly within the ecosystem. By simulating countless interactions, from player behaviors around RWAs to how creators are compensated for their NFTs, we’re building a system that feels balanced and engaging. Players will feel motivated to complete challenges, not just for virtual rewards, but for the tangible, long-term achievement of RWAs. Meanwhile, creators will be empowered to contribute high-quality UGC, knowing that they will be fairly compensated for the value they bring to the AR world.
By refining these mechanisms through RL agents, we're ensuring that the AR platform is not just another game—it’s a thriving, dynamic ecosystem where creativity, engagement, and rewards are all intertwined.
