Reinforcement Learning is at the core of building and improving frontier AI models and products. Yet most state-of-the-art RL methods learn primarily from outcomes: a scalar reward signal that says ...

In reinforcement learning (RL), an agent learns to achieve its goal by interacting with its environment and learning from feedback about its successes and failures. This feedback is typically encoded ...

Over the past few years, AI systems have become much better at discerning images, generating language, and performing tasks within physical and virtual environments. Yet they still fail in ways that ...

In this tutorial, we code a mini reinforcement learning setup in which a multi-agent system learns to navigate a grid world through interaction, feedback, and layered decision-making. We build ...

The rapid evolution of modern electric power distribution systems into complex networks of interconnected active devices, distributed generation (DG), and storage poses increasing difficulties for ...

In this tutorial, we explore advanced applications of Stable-Baselines3 in reinforcement learning. We design a fully functional, custom trading environment, integrate multiple algorithms such as PPO ...

Thinking about learning Python? It’s a pretty popular language these days, and for good reason. It’s not super complicated, which is nice if you’re just starting out. We’ve put together a guide that ...

Nearly a century ago, psychologist B.F. Skinner pioneered a controversial school of thought, behaviorism, to explain human and animal behavior. Behaviorism directly inspired modern reinforcement ...

In the 1980s, Andrew Barto and Rich Sutton were considered eccentric devotees to an elegant but ultimately doomed idea—having machines learn, as humans and animals do, from experience. Decades on, ...