Why ARC Agents Will Break Through the Current Experience of AI Games

Author: Teng Yan, Chain of Thought; Translation: Golden Finance xiaozou

In 2021, I was still an Axie Infinity player and ran a small scholarship guild. If you haven't experienced that era, let me tell you - it's absolutely wild.

Axie Infinity is a game that makes people realize that cryptocurrencies and games can be combined. Essentially, it is a simple Pokémon-style strategy game where players need to form a team of 3 Axies (very fierce fighters), each with unique abilities. You can lead your team to fight other teams and get SLP token rewards by participating in the game and winning.

But what really got non-gamers excited was the potential to make money through gaming. Axie’s meteoric rise was made possible by two major mechanisms:

The first was Breeding Axies. Take two Axies, breed them using SLP tokens, and voilà—a new Axie that combines the unique abilities of the two original Axies. Such rare and powerful Axies (which gamers call OP Axies) became a hot commodity, and a busy breeding market emerged.

The second mechanism was the scholarship program. Corporate players from around the world began lending Axies to “scholars.” These players are often from developing countries like the Philippines or Argentina, who can’t afford the $1,000+ upfront fee to buy three Axie NFTs. Scholars earn tokens playing games every day and share the profits with the scholarship guild, which typically takes a 30-50% cut.

In its heyday, especially during the COVID-19 pandemic, Axie had a significant impact on local economies in developing countries. In the Philippines (where about 40% of Axie Infinity users are based), many players make well above the minimum wage. Guilds profit handsomely.

This solves a key problem for game developers: player liquidity. By incentivizing players to actively play the game for a few hours a day, Axie ensures that every player will have an opponent waiting there, making the player experience more engaging.

But it comes at a cost.

To solve the player liquidity problem, Axie gives away a lot of tokens to incentivize players to participate. The story begins here. Since SLP has no cap, tokens inflate wildly, prices plummet, and the ecosystem collapses. Tokens lose value, and players leave. Axie went from a “play to earn” darling to a cautionary tale almost overnight.

But what if there was a way to solve the player liquidity problem without unsustainable token economics?

This is exactly what ARC/AI Arena has been quietly working on for the past three years. Now, it’s starting to bear fruit.

1Player liquidity is the lifeblood

Player liquidity is the lifeblood of multiplayer games and the key to long-term success.

Many Web3 and indie games face the “cold start” problem — too few players to quickly matchmake or form a thriving community. They don’t have the marketing budgets or natural IP awareness that the big game companies have. This leads to long wait times, inability to matchmake, and high churn rates.

These games usually die a slow and painful death.

Therefore, game developers must prioritize player liquidity from the beginning. Games require activities of one kind or another to maintain fun - chess requires two players, and large-scale battles require thousands of players. Skill matching mechanisms further raise the bar, requiring more players to keep the game fair and attractive.

For Web3 games, the stakes are even higher. According to Delphi Digital’s annual game report, user acquisition costs for Web3 games are 77% higher than traditional mobile games, making player retention critical.

A strong player base ensures fair matching, a vibrant game economy (i.e. more props buying and selling), and more active social interactions, making the game more interesting.

2ARC-AI Game Pioneer

ARC, developed by ArenaX Labs, is leading the future of AI online gaming experiences. In short, they use AI to solve the player flow problem that plagues new games.

The problem with most AI bots in games today is that they are terrible. Once you spend a few hours getting the hang of it, these bots become incredibly easy to beat. They are designed to help new players, but don’t offer much challenge or engagement for experienced players.

Imagine AI players with skills that rival those of top human players. Imagine being able to play against them anytime, anywhere, without having to wait for matchmaking. Imagine training your AI player to mimic your play style, owning it, and earning rewards for its performance.

It’s a win-win for both players and game companies.

Game companies use human-like AI bots to make games popular, increase player flow, improve user experience, and boost retention—critical factors for new game entrants to survive in a competitive market.

Players gain a new way to engage with games, building a stronger sense of ownership in the process of training and playing against the AI.

Let’s take a look at how they do it.

3, Products and Architecture

Parent company ArenaX Labs is developing a series of products to solve player liquidity issues.

• Existing products: AI Arena, an AI fighting game.

• New products: ARC B2B, an AI-driven game SDK that can be easily integrated into any game.

• New Product: ARC Reinforcement Learning (RL)

(1) AI Arena: The Game

AI Arena is a fighting game reminiscent of Nintendo’s Super Smash Bros, where a variety of quirky cartoon characters battle in an arena.

But in AI Arena, each character is controlled by AI—you play not as a fighter, but as their trainer. Your task is to use your strategies and expertise to train your AI fighters.

Training your fighters is like training a student to prepare for battle. In training mode, you turn on data collection and create combat scenarios to fine-tune their moves. For example, if your fighter is close to an opponent, you can teach them to block with your shield and then combo. How about fighting at a distance? Train them to launch ranged attacks.

You control what kind of data is collected, ensuring only the best moves are recorded for training. With practice, you can refine the hyperparameters to gain more technical advantages, or simply use the beginner-friendly default settings. Once training is complete, your AI fighter is ready for combat.

Beginning is difficult - training an effective model takes time and experimentation. My first fighter fell off the platform several times, not because of being hit by the opponent. But after a few iterations, I was able to create a model that performs well. It's deeply satisfying to see your training pay off.

AI Arena introduces additional depth through NFT fighters. Each NFT character has unique appearance characteristics and combat attributes that affect gameplay. This adds another layer of strategy.

Currently, AI Arena runs on the Arbitrum mainnet and is only accessible to those who have the AI ​​Arena NFT, maintaining community exclusivity while improving gameplay. Players can join guilds, gather Champion NFTs and NRN to rank up in on-chain battles, and earn rewards. This is done to attract loyal players and drive competition.

Ultimately, AI Arena is a showcase for ARC’s AI training technology. While it is their entry point into the ecosystem, the true vision goes far beyond this game.

(2) ARC: Infrastructure

ARC is an AI infrastructure solution designed specifically for gaming.

The ArenaX team started from scratch, even developing their own gaming infrastructure, because existing solutions like Unity and Unreal could not meet their vision.

Over three years, they crafted a robust technology stack capable of handling data aggregation, model training, and model checking for imitation and reinforcement learning. This infrastructure is the backbone of AI Arena, but its potential is much greater.

As the team continued to refine their technology, third-party studios began to approach ARC, looking to license or white-label the platform. Recognizing this need, they formalized ARC's infrastructure as a B2B product.

Today, ARC works directly with game companies to deliver AI gaming experiences. The value proposition is:

• Permanent Player Liquidity as a Service

• AI Gameplay as a Simple Integration

Permanent Player Liquidity as a Service

ARC focuses on human behavior cloning - training specialized AI models to mimic human behavior. This is different from the main uses of AI in games today, which use generative models to create game assets and LLMs to drive dialogue.

Using the ARC SDK, developers can create human-like AI agents and scale them to meet the needs of their games. The SDK simplifies the heavy lifting. Game companies can introduce AI without dealing with the complexities of machine learning.

Once integrated, deploying an AI model requires only one line of code, and ARC takes care of the infrastructure, data processing, training, and backend deployment.

ARC works with game companies to help them:

• Capture raw gameplay data and transform it into meaningful datasets for AI training.

• Identify key gameplay variables and decision points related to game mechanics.

• Map AI model outputs to in-game activities to ensure smooth functionality—for example, linking an AI’s “right click” output to a specific game control.

How does AI work?

ARC uses four types of models for game interactions:

• Feedforward Neural Networks: For continuous environments with numerical features like velocity or position.

• Table Agents: Ideal for games with finite, discrete scenes.

• Hierarchical and convolutional neural networks are in development.

There are two interactive spaces relevant to ARC’s AI models:

The state space defines what the agent knows about the game at any given moment. For a feedforward network, this is the combination of input features (like the player’s velocity or position). For a form agent, this is the discrete scenarios the agent might encounter in the game.

The action space describes what the agent can do in the game, from discrete inputs (like pressing a button) to continuous controls (like a joystick move). This maps to game inputs.

The state space provides input to ARC’s AI models, which process the input and generate outputs. These outputs are then transformed into game actions via the action space.

ARC works closely with game developers to identify the most critical features and design the state space accordingly. They also test various model configurations and sizes to balance intelligence and speed, ensuring that the game operates smoothly and is engaging.

According to the team, demand for their player liquidity services is particularly high among web3 companies. These companies pay for better player liquidity, and ARC will use a large portion of this revenue for NRN token buybacks.

Bringing AI gameplay to players: Trainer Platform

The ARC SDK also gives web3 companies access to a Trainer Platform for their games, allowing players to train and submit agents.

As with AI Arena, players can set up simulations, obtain gameplay data, and train blank AI models. These models will evolve over time, incorporating new gameplay data while retaining previous knowledge, without having to start from scratch with each update.

This opens up exciting possibilities: players can sell their custom-trained AI agents on the marketplace, creating a new in-game economic layer. In AI Arena, skilled trainers can form guilds where they can offer their training skills to other companies.

For companies that fully integrate agent functionality, the concept of Parallel Play also comes to life. AI agents are available 24/7 and can participate in multiple matches or game instances at the same time. This solves the liquidity problem for players and creates new opportunities for user stickiness and monetization.

But that’s not all…

(3) ARC RL: From One-to-One to Many-to-One

If AI Arena and the ARC Trainer Platform feel like single-player modes (where you can train your own AI model), then ARC RL is akin to multiplayer modes.

Imagine this: an entire game DAO pools its gameplay data to train a shared AI model, and everyone jointly owns and benefits from the model. These “master agents” represent the collective intelligence of all players, transforming esports by introducing competition driven by collective effort and strategic collaboration. ARC RL uses reinforcement learning (or “RL”) and crowdsourced human gameplay data to train these “super-intelligent” agents. Reinforcement learning works by rewarding agents for optimal behavior. It works particularly well in games because the reward function is clear and objective, such as damage caused, gold gained, or wins. There is precedent for this: DeepMind’s AlphaGo beat professional human players at the game of Go, training through millions of self-generated games, perfecting its strategy with each iteration. I didn’t realize this before, but OpenAI was already well-known in gaming circles long before chatGPT was created. OpenAI Five used reinforcement learning to crush top human players in Dota 2 and beat the world champion in 2019. It mastered advanced strategies such as teamwork through accelerated simulations and massive computing resources.

OpenAI Five runs millions of games per day, the equivalent of 250 years of simulated games per day, powered by 256 GPUs and 128,000 CPUs. By skipping graphics rendering, it greatly speeds up learning.

Initially, the AI ​​exhibited erratic behavior, such as wandering aimlessly, but quickly improved. It mastered basic strategies, such as creeping on trails and stealing resources, and eventually developed complex maneuvers, such as ambushes.

The key idea of ​​reinforcement learning is that an AI agent learns how to succeed through experience, rather than being directly told what to do.

ARC RL sets itself apart by using offline reinforcement learning. Instead of learning from their own trial and error, AI agents learn from the experience of others. It's like a student watching videos of others riding a bicycle, observing their successes and failures, and using that knowledge to avoid falling and improve faster.

This approach provides an additional benefit: collaborative training and shared ownership of models. This not only makes powerful AI agents more accessible, but also better aligns the incentives of players, guilds, and developers.

There are two key roles in the creation of “super-intelligent” gaming agents:

• Sponsors:Guild-like leaders who stake large amounts of NRN tokens to launch and manage RL agents. Sponsors can be any entity, but are likely gaming guilds, DAOs, web3 communities, or even popular on-chain personalized agents like Luna.

• Players:Individuals who stake small amounts of NRN tokens to contribute their gameplay data to train the agent.

Sponsors coordinate and guide their team of players, ensuring high-quality training data that gives their AI agents a competitive edge in agent competitions.

Rewards are distributed based on the performance of the super agent in the competition. 70% of the rewards go to the players, 10% to the sponsors, and the remaining 20% ​​to the NRN treasury. This structure aligns incentives for all participants.

Data Contribution

How do you get players to happily contribute their gameplay data? Not easy.

ARC makes contributing gameplay data simple and rewarding. Players don’t need expertise, just play the game. After a session, they are prompted to submit data to train a specific agent. A dashboard tracks their contributions and the agents they supported.

ARC’s attribution algorithm ensures quality by evaluating contributions and rewarding high-quality, impactful data.

Interestingly, even if you’re a terrible player (like me), your data is useful. Bad gameplay can help agents learn what not to do, while skilled gameplay can teach optimal strategies. Redundant data is filtered out to preserve quality.

In short, ARC RL is designed to be a low-friction, mass-market product centered around the shared ownership of agents that surpass human capabilities.

4、Market Size

ARC's technology platform is versatile and supports multiple types of games, such as shooters, fighting games, social casinos, racing, card trading games, and RPGs. It is tailored for games that need to maintain player stickiness.

ARC's products are mainly aimed at two markets:

ARC focuses on independent developers and companies rather than established big companies. Due to limited brand influence and distribution resources, these small companies usually find it difficult to attract players in the early stages.

ARC's AI agent solves this problem by creating a dynamic gaming environment from the beginning, ensuring dynamic gameplay even in the initial stages of the game.

This may surprise many people, but the independent game sector is indeed a major force in the game market:

• 99% of games on Steam are independent games.

• In 2024, independent games generated 48% of total revenue on Steam.

Another target market is Web3 games. Most Web3 games are developed by emerging companies, and they also face unique challenges such as wallet login, encryption doubts, and high user acquisition costs. These games often have player liquidity issues, and AI agents can fill the gap and keep the game engaging.

While Web3 games have struggled recently due to a lack of engaging experiences, they are showing signs of recovery.

For example, Off the Grid, one of the earliest AAA Web3 games, recently achieved early mainstream success, with 9 million wallets conducting 100 million transactions in its first month. This paves the way for widespread success in the industry and creates an opportunity for ARC to support this resurgence.

5. ARC Team

The founding team behind ArenaX Labs has extensive expertise in machine learning and investment management.

CEO and CTO Brandon Da Silva previously led machine learning research at a Canadian investment firm, focusing on reinforcement learning, Bayesian deep learning, and model adaptability. He pioneered the development of a $1 billion quantitative trading strategy centered on risk parity and multi-asset portfolio management.

COO Wei Xie manages a $7 billion liquidity strategy portfolio at the same company and leads its innovative investment projects, focusing on emerging fields such as AI, machine learning and Web3 technology.

ArenaX Labs received a $5 million seed round in 2021, led by Paradigm and participated by Framework ventures. The company received $6 million in financing in January 2024, led by SevenX Ventures, FunPlus/Xterio and Moore Strategic Ventures.

6NRN Token Economics - A Healthy Reform

ARC/AI Arena has a token - NRN. Let's take stock of the current situation.

Examining the supply side and the demand side will give us a clearer understanding of the trend.

(1) Supply side

The total supply of NRN is 1 billion, of which about 409 million (40.9%) are in circulation.

At the time of writing, the token is priced at $0.72, which means a market cap of $29 million and a fully diluted valuation of $71 million.

NRN was launched on June 24, 2024, with 40.9% of the circulating supply coming from:

• Community Airdrop (8% of total)

• Foundation Treasury (10.9%, with 2.9% unlocked, unlocking linearly over 36 months)

• Community Ecosystem Rewards (30%)

The majority of circulating supply (30% of 40.9%) consists of Community Ecosystem Rewards, which the project manages and strategically allocates to staking rewards, gaming rewards, ecosystem growth initiatives, and community-driven initiatives.

The unlocking schedule is reassuring, with no major events in the near term:

• The next unlock is the Foundation's OTC sale (1.1%), unlocking linearly over 12 months starting in December 2024. This will only increase monthly inflation by 0.09% and is unlikely to be a cause for major concern.

• The allocation to investors and contributors (50% of the total supply) will not begin unlocking until June 2025, and even then, it will be unlocked linearly over 24 months.

For now, selling pressure is expected to remain fairly manageable, primarily stemming from ecosystem rewards. The key is to trust the team’s ability to strategically deploy these funds to drive growth for the protocol.

(2) Demand Side

NRN v1——Player Economy

Originally, NRN was designed as a strategic resource associated with the AI ​​Arena game economy.

Players stake NRN on AI players and are rewarded if they win, and lose part of their stake if they lose. This creates a direct stakes dynamic, turning it into a competitive sport and providing financial incentives for skilled players.

Rewards are distributed using the ELO system, ensuring balanced payouts based on skill. Other sources of revenue include in-game item purchases, cosmetic upgrades, and tournament entry fees.

The initial token model is entirely dependent on the success of the game and the continued willingness of new players to purchase NRN and NFTs to participate in the game.

Here’s why we’re so excited…

NRN v2—Player & Platform Economy

NRN’s improved v2 token economics introduces powerful new demand drivers by expanding the token’s utility from AI Arena to the broader ARC platform. This evolution transforms NRN from a specific game token to a platform token. In my opinion, this is a very positive shift.

NRN’s three new demand drivers include:

• Revenue from ARC integration. Game companies that integrate ARC will generate revenue for the Treasury through integration fees and ongoing royalties tied to game performance. Treasury funds can drive NRN buybacks, grow the ecosystem, and incentivize players on the Trainer Platform.

• Trainer Marketplace Fees. NRN derives value from fees charged on the Trainer Marketplace, where players can trade AI models and gameplay data.

• Participate in Staking on ARC RL: Both sponsors and players must stake NRN to join ARC RL. As more players enter ARC RL, demand for NRN increases accordingly.

Especially exciting is the revenue from game companies. This marks a shift from a purely B2C model to a hybrid B2C and B2B model, creating a continuous inflow of external capital into the NRN economy. With ARC having a broader target market, this revenue stream will exceed what AI Arena can generate on its own.

Fees on the trainer market, while promising, depend on the ecosystem reaching critical mass — enough games, trainers, and players to sustain active trading activity. This is a long-term business.

In the short term, ARC RL staking is likely to be the most immediate and reflexive demand driver. A well-funded initial reward pool and excitement around a new product launch could spark early adoption, driving up token prices and attracting participants. This creates a feedback loop of rising demand and economic growth. However, looking the other way, if ARC RL struggles to maintain user stickiness, demand could quickly evaporate.

The potential for network effects is huge: more games → more players → more games joining → more players. This virtuous cycle can position NRN as a core token in the Crypto AI gaming ecosystem.

7The Mother of Game AI Models

What is the endgame? ARC’s strength lies in its ability to promote a wide range of game types. Over time, it allows them to collect a unique database of specific gameplay. As ARC integrates with more games, it can continually feed this data back into its own ecosystem, creating a virtuous cycle of growth and improvement.

Once this cross-sectional game dataset reaches critical mass, it will become a very valuable resource. Imagine using it to train a general AI model for game development - opening up new possibilities for designing, testing, and optimizing games at scale.

It’s still early days, but in the age of AI where data is the new oil, the potential is endless.

8Our Thoughts

(1) NRN Evolves into a Platform Game - Token Repricing

With the release of ARC and ARC RL, the project is no longer just a single-product gaming company, it now positions itself as a platform and AI gaming. This shift should lead to a re-rating of the NRN token, which was previously limited to the success of AI Arena. The introduction of a new token source through ARC RL, coupled with revenue sharing agreements with gaming companies and external demand for trainer transaction fees, creates a broader and more diverse base for NRN's utility and value.

(2) Success is closely tied to gaming partners

ARC's business model ties its success to the companies it partners with, as the revenue stream is based on token distribution (in Web3 games) and payment of gaming royalties. Games that are closely aligned with it are worth a look.

If ARC games are wildly successful, the resulting value will flow back to NRN holders. Conversely, if partnered games struggle, the value stream will be limited.

(3) Expect more integration with Web3 games

The ARC platform is a great fit for Web3 games, where competitive gameplay with incentives is perfectly integrated with existing token economies.

By integrating ARC, Web3 games can immediately enter the “AI agent” narrative. ARC RL brings communities together and incentivizes them to work towards a common goal. This also opens up new opportunities for innovative mechanisms, such as making events like “Game to Airdrop” more appealing to players. By combining AI and token incentives, ARC adds depth and excitement that traditional games cannot replicate.

(4) AI gameplay has a learning curve

AI gameplay has a steep learning curve, which can cause friction for new players. It took me an hour to figure out how to properly train my players in AI Arena.

However, ARC RL has a lower friction player experience because AI training is handled on the backend as players play and submit data. Another open question is how players will feel when they know their opponents are AI. Will this have an impact on them? Will it enhance or detract from the gaming experience? Only time will tell.

9A Bright Future

AI will open up new and groundbreaking experiences in the gaming world.

Teams like Parallel Colony and Virtuals are pushing the boundaries of autonomous AI agents, while ARC is carving out its own niche by focusing on human behavior cloning - offering an innovative approach to solving player liquidity challenges without relying on unsustainable token economics.

The transition from gaming to a full-fledged platform is a huge leap for ARC. This not only opens up greater opportunities through partnerships with gaming companies, but also reconstructs the way AI is integrated with gaming.

With its improved token economics and the potential for strong network effects, ARC's bright road seems to have just begun.