Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025) ...
Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...
A quadruped robot uses deep reinforcement learning to master walking on varied terrains, demonstrating energy-efficient and ...
Among those interviewed, one RL environment founder said, “I’ve seen $200 to $2,000 mostly. $20k per task would be rare but ...
Imagine trying to teach a child how to solve a tricky math problem. You might start by showing them examples, guiding them step by step, and encouraging them to think critically about their approach.
What if the very techniques we rely on to make AI smarter are actually holding it back? A new study has sent shockwaves through the AI community by challenging the long-held belief that reinforcement ...
In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...
Prior machine learning experience (e.g., an introductory machine learning course ELEC_ENG 375/475 or COMP_SCI 349 or a similar course), a thorough understanding of Linear Algebra and Vector Calculus, ...
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