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.
A recent study published in Engineering presents a significant advancement in manufacturing scheduling. Researchers Xueyan Sun, Weiming Shen, Jiaxin Fan, and their colleagues from Huazhong University ...
Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025) ...
DeepSeek-R1's release last Monday has sent shockwaves through the AI community, disrupting assumptions about what’s required to achieve cutting-edge AI performance. Matching OpenAI’s o1 at just 3%-5% ...
A quadruped robot uses deep reinforcement learning to master walking on varied terrains, demonstrating energy-efficient and ...
Interesting Engineering on MSN
AI-trained quadruped robot walks rough, low-friction terrain without human input
A quadruped robot has learned to walk across slippery, uneven terrain entirely through simulation, ...
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