AI Agents

How can AI groups run Tinker type reinforcement studying on massive language fashions utilizing their very own infrastructure with a single unified engine? Anyscale and NovaSky (UC Berkeley) Team releases SkyRL tx v0.1.0 that provides builders a technique to run a Tinker suitable coaching and inference engine instantly on their very own {hardware}, whereas maintaining…

In this tutorial, we discover how to construct an clever agent that remembers, learns, and adapts to us over time. We implement a Persistent Memory & Personalisation system utilizing easy, rule-based logic to simulate how fashionable Agentic AI frameworks retailer and recall contextual data. As we progress, we see how the agent’s responses evolve with…

In this tutorial, we construct an Agentic Data and Infrastructure Strategy system utilizing the light-weight Qwen2.5-0.5B-Instruct mannequin for environment friendly execution. We start by creating a versatile LLM agent framework and then develop specialised brokers that deal with totally different layers of knowledge administration, from ingestion and high quality evaluation to infrastructure optimization. We combine…

In this tutorial, we discover how we are able to construct an autonomous agent that aligns its actions with moral and organizational values. We use open-source Hugging Face fashions operating domestically in Colab to simulate a decision-making course of that balances aim achievement with ethical reasoning. Through this implementation, we show how we are able…

In this tutorial, we discover how exploration methods form clever decision-making via agent-based downside fixing. We construct and prepare three brokers, Q-Learning with epsilon-greedy exploration, Upper Confidence Bound (UCB), and Monte Carlo Tree Search (MCTS), to navigate a grid world and attain a aim effectively whereas avoiding obstacles. Also, we experiment with alternative ways of…

In this tutorial, we discover superior functions of Stable-Baselines3 in reinforcement studying. We design a totally practical, customized buying and selling atmosphere, combine a number of algorithms equivalent to PPO and A2C, and develop our personal coaching callbacks for efficiency monitoring. As we progress, we prepare, consider, and visualize agent efficiency to evaluate algorithmic effectivity,…

In this tutorial, we construct a sophisticated computer-use agent from scratch that can motive, plan, and carry out digital actions utilizing a native open-weight mannequin. We create a miniature simulated desktop, equip it with a device interface, and design an clever agent that can analyze its surroundings, resolve on actions like clicking or typing, and…