Nature

Deep Reinforcement Learning for Active Flow Control

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.
Devdiscourse

AI can cut emissions and congestion in urban transport by nearly 50%

Traffic congestion, fuel consumption, and emissions also offer quantifiable performance indicators, making mobility uniquely ...
mccormick.northwestern.edu

ELEC_ENG 373, 473: Deep Reinforcement Learning from Scratch

Prior deep learning experience (e.g. ELEC_ENG/COMP_ENG 395/495 Deep Learning Foundations from Scratch ) and strong familiarity with the Python programming language. Python will be used for all coding ...
Geeky Gadgets

OpenAI Introduces Reinforcement Fine-Tuning (RFT) for Easy AI Customization

Have you ever wished AI could truly understand the complexities of your field—not just replicate data but reason through intricate, domain-specific challenges? Whether you’re a researcher analyzing ...