Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025) ...
Interesting Engineering on MSN
AI-trained quadruped robot walks rough, low-friction terrain without human input
This multi-objective setup encourages natural walking behavior rather than rigid or inefficient movement. A four-stage ...
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 ...
Data-driven insights to support veterinary practices, advisors, and multi-site organizations planning for the year ...
In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...
Most current autonomous driving systems rely on single-agent deep learning models or end-to-end neural networks. While ...
For autonomous mobile robots to operate effectively in human environments, navigation must extend beyond obstacle avoidance to incorporate social awareness. Safe and fluid interaction in shared spaces ...
NVIDIA's ToolOrchestra employs small orchestration agents to optimize AI tasks, achieving superior performance and cost-efficiency. Discover how this innovation is reshaping AI paradigms. In a ...
ABSTRACT: Multi-objective optimization remains a significant and realistic problem in engineering. A trade-off among conflicting objectives subject to equality and inequality constraints is known as ...
Abstract: Visual reinforcement learning (VRL) aims to learn optimal policies directly from pixel data, which holds significant potential for applications in control systems characterized by data ...
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