Hey I am not writing this with AI by the way. I value your time and this is short and sweet :) - Flow RL dev

THIS PROJECT USES FLOW, is a CONSUMER APP AND PROTOCOL, and USES FLOW ACTIONS. It is in environments -> community, and it's called RL Marketplace. The public RL gym framework this was built on is called Atropos, and that's what the rest of the repo is.

Flow RL (Reinforcement Learning) creates a market for valuable data generated by Flow, by creating a general framework to take any onchain data and make it palatable for RL training.

Here is a demo video: https://www.loom.com/share/536cb33b3d7d4af5a32022c5fa0c1604

In an AI future, blockchains need to radically change the way they think about the data generated by their protocol, since all that data is useful for AI training. By creating a competition around generating the best data, Flow can outcompete on this front by incentivizing exceptional onchain and agentic behaviors. The key to this is a general framework for generating rich reward signals models can use. I think blockchains need to adapt sooner rather than later and figuring out this AI data play could position Flow really well.

This MVP successfully went from onchain activity -> valuable reward signals that AI labs like OpenAI and Anthropic spend $100M on yearly!

1. Flow integration - Contracts were successfully deployed on Flow testnet.
2. FlowActions - in WordHunt.cdc I used Flow Actions Sink to collect the fee to participate, seperating the logic from the game. Sean from Flow helped with this.
3. Atropos initiates the program and provides the contract with unique game boards and the right prompt. The contract supplies the answers, and the actual calculation of scores that get thrown into the reward function and the solving of the board is done off-chain.
4. The logic to score the word hunt board was implemented by me (I love the game and am very familiar). I use a trie data structure to check for valid words.
5. Atropos makes it easy to train a model once you have the reward signals, so this project went from onchain activity directly to perfectly expressive reward signals.
6. I added the onchain integration with Atropos myself, it wasn't meant to be used by agentic systems.

Dependencies:

Flask requests pydantic transformers torch huggingface_hub tqdm accelerate