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Reinforcement Learning

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.

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About Reinforcement Learning


Reinforcement Learning AI technology page Hackathon projects

Discover innovative solutions crafted with Reinforcement Learning AI technology page, developed by our community members during our engaging hackathons.

Quantified Ubuntu Contribution Integrity Crucible

Quantified Ubuntu Contribution Integrity Crucible

The Problem: Moral Failure and Stagnation (Focus: Relatable) Open-source healthcare innovation is crippled by subjective recognition, slow development, and the moral failure of systems prioritizing profit over patient care. Developers lack objective metrics for contribution quality, leading to burnout and talent exclusion based on static credentials, not real-world impact (e.g., the 900x faster MCP Server). The Solution: QUBIC's Meritocratic Architecture (Focus: Mechanics) QUBIC is the solution. It is a hybrid, autonomous governance protocol built for the Nostromo Launchpad. Off-Chain AI Analysis: Our AI Judge (LLM-Powered) scores every contribution (code quality, documentation, clinical utility, security compliance) against a public, weighted rubric. On-Chain Integrity: A secure Oracle posts the composite score to the Qubic C++ Smart Contract DAO. Trustless Rewards: The Smart Contract registers the contributor with a Platinum, Gold, or Silver tier and automatically distributes UC tokens and weighted voting power. The Qubic Advantage (Focus: Technical Edge) QUBIC leverages Qubic's high-performance C++ architecture for fast, deterministic execution, essential for mission-critical healthcare governance. This ensures that the DAO remains a Meritocratic Democracy—where governance power is based on proven, life-saving contributions, not token wealth. QUBIC is the trust layer that funds and sustains the Ubuntu Patient Care Rebellion.

AI-Powered Chest X-Ray Classification

AI-Powered Chest X-Ray Classification

This project presents an AI-powered chest X-ray classification system designed to assist in the early detection of pneumonia. We trained a ResNet101 deep learning model on chest X-ray images and achieved strong accuracy in distinguishing between Normal and Pneumonia cases. The trained model was converted into a TorchScript (.pt) format for portability and deployed using a Flask web application. Users can upload their chest X-ray images, and the system instantly provides a prediction, along with a user-friendly interface. The project demonstrates the potential of AI in healthcare by providing fast, reliable, and accessible diagnostic support. Future improvements could include multi-disease classification, integration with cloud platforms, and real-time hospital deployment. 🔹 Problem Statement: Pneumonia is one of the leading causes of mortality worldwide, especially in children and the elderly. Traditional diagnosis through chest X-rays requires expert radiologists, which may not always be accessible in rural or under-resourced areas. Delayed diagnosis can lead to severe complications or death. There is a critical need for fast, accessible, and affordable tools to assist healthcare providers in detecting pneumonia. 🔹 Solution: We developed an AI-based chest X-ray classification system using ResNet101, trained to distinguish between Normal and Pneumonia cases. The model was exported into a TorchScript (.pt) format for lightweight deployment and integrated into a Flask web application. Doctors or patients can simply upload a chest X-ray image, and the system instantly returns a prediction. 🔹 Impact: Accessibility: Makes pneumonia detection available to clinics with limited radiology staff. Speed: Provides predictions in seconds, reducing diagnosis time. Scalability: Can be deployed globally via the web and reaching rural. Future Scope: Extendable to detect multiple lung diseases, integration with cloud-based hospital systems, and mobile app deployment.

Sellene

Sellene

We are building a decentralized platform that transforms how creative works—songs, artworks, stories, inventions, and more—are shared, owned, and monetized. Instead of relying on traditional gatekeepers like publishers or labels, creators can tokenize their work, offering small digital shares to fans, backers, and investors. Each registered work is split into a fixed number of tokens, representing fractional ownership. These tokens can be bought, traded, or held by anyone. For example, a musician can tokenize a new song into 1,000 shares. Supporters who purchase tokens earn a proportional share of future royalties whenever the song is licensed, streamed, or sold. Investors benefit from a marketplace where creative IP becomes a new asset class. Token holders automatically receive royalty payouts through smart contracts—no delays, no intermediaries. Every transaction is recorded on-chain, ensuring transparency and trust. The platform includes cross-chain functionality via DeBridge and TomoChain, enabling users to connect various wallets and trade assets across multiple chains like Ethereum, Polygon, BSC, and TomoChain. Social login and wallet aggregation make it easy for creators and users to participate, even without deep Web3 knowledge. Story Protocol integration ensures proper IP registration, metadata storage, and IP discovery. Creators retain sovereignty over their works while maintaining full control over how much to sell and at what price. Gaia agents provide autonomous protection, licensing enforcement, and usage verification. AWE Network agents manage bridge strategies and world state logic—ensuring seamless cross-chain interaction and IP rule enforcement. This is a creator-first economy where ownership is shared, revenue is fair, and creativity becomes an investable commodity.