Robust Reinforcement Learning-based model for UAV self-separation under Uncertainty. Hybrid; Amsterdam , Noord-Holland , Netherlands; Aerosp ...

Reinforcement learning has become the central approach for language models (LMs) to learn from environmental reward or feedback. In practice, the environmental feedback is usually sparse and delayed.

Study authors Hunter Schweiger (left) and Ash Robbins. Imagine balancing a ruler vertically in the palm of your hand: you have to constantly pay attention to the angle of the ruler and make many small ...

Leaders, whether in boardrooms or garages, constantly face an unchanging force: uncertainty. For a CEO, making a good decision always involves factoring in as much data as possible, and then trusting ...

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 ...

Every year, NeurIPS produces hundreds of impressive papers, and a handful that subtly reset how practitioners think about scaling, evaluation and system design. In 2025, the most consequential works ...

Watch an AI agent learn how to balance a stick—completely from scratch—using reinforcement learning! This project walks you through how an algorithm interacts with an environment, learns through trial ...

How can a small model learn to solve tasks it currently fails at, without rote imitation or relying on a correct rollout? A team of researchers from Google Cloud AI Research and UCLA have released a ...