A new discovery of how bees use their flight movements to facilitate remarkably accurate learning and recognition of complex ...

One major problem for on-robot reinforcement learning is ensuring safety, as uncontrolled exploration can cause catastrophic damage to the robot or the environment. Indeed, safety specifications, ...

About official repository for “Reinforcement Learning for Reasoning in Large Language Models with One Training Example” Readme Apache-2.0 license ...

A machine learning approach leverages nuclear microreactor symmetry to reduce training time when modeling power output ...

Learn how to train a neural network to recognize hand-drawn digits using PyTorch! A fun and beginner-friendly intro to deep learning and computer vision.

MiniMax-M1 presents a flexible option for organizations looking to experiment with or scale up advanced AI capabilities while managing costs.

Our findings suggest that people who are already doing extremely well—like professional mathematicians or a mental calculation world champion—do not benefit from tRNS. In contrast, those who are below ...

Researchers from Nanjing University and UC Berkeley have unveiled a clustering-based reinforcement learning framework that balances novelty and ...

Explore news and insights on climate change, environment policies, global warming and sustainable practices with The New York Times Climate and Environment section.

This paper proposes a novel environment-adaptive motion planning framework for mobile robots, which utilizes deep reinforcement learning to dynamically adjust optimization objectives according to ...