Deep reinforcement learning (DRL) has emerged as a transformative approach in the realm of fluid dynamics, offering a data-driven framework to tackle the intrinsic complexities of active flow control.

The core of this research lies in the combination of Graph Neural Networks (GNN) and Reinforcement Learning to achieve coordinated control of up to eight robotic arms, enabling efficient and collision ...

According to IPO Early News, Baidu's Chief Technology Officer and Director of the National Engineering Research Center for ...

The paper “Deep Reinforcement Learning-based Multi-Objective Scheduling for Distributed Heterogeneous Hybrid Flow Shops with Blocking Constraints,” authored by Xueyan Sun, Weiming Shen, Jiaxin ...

DeepSeek-R1’s Monday release has sent shockwaves through the AI community, disrupting assumptions about what’s required to achieve cutting-edge AI performance. This story focuses on exactly ...

The conventional approach to learning to play games involves training neural networks through what is known as deep reinforcement learning, which involves experimenting and tweaking their ...

Reinforcement learning techniques could be the keys to integrating robots — who use machine learning to output more than words — into the real world.

Many existing reinforcement-learning techniques require a whole separate model to make this calculation.

Deep Think builds on the version of Google’s Gemini 2.5 model that recently clinched a gold medal standard at the 2025 International Mathematical Olympiad (IMO).