Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025)

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 ...
AZoRobotics on MSN

Simulation-only training enables quadruped robots to master real-world terrain

A quadruped robot uses deep reinforcement learning to master walking on varied terrains, demonstrating energy-efficient and ...
EurekAlert!

A new AI-based attack framework advances multi-agent reinforcement learning by amplifying vulnerability and bypassing defenses

Researchers have developed a novel framework, termed PDJA (Perception–Decision Joint Attack), that leverages artificial ...
EurekAlert!

Deep reinforcement learning optimizes distributed manufacturing scheduling

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 ...
Electronics360

Wind turbine control systems: From PID to reinforcement learning

In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...