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 ...
IEEE

LOFT: An LLM-Enhanced Multi-Objective Search Framework for Fault Injection Testing of Autonomous Driving Systems

Abstract: Autonomous Driving Systems (ADS) are considered safety-critical, as even a minor fault may lead to catastrophic consequences. To evaluate their reliability and robustness under failure ...
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 ...
IEEE

Towards Ecological Decision-making on Highways: A Multi-objective Eco-driving Framework for Connected FCHEVs via Offline-to-Online Reinforcement Learning

Abstract: Leveraging vehicle-to-everything (V2X) and artificial intelligence (AI), fuel cell hybrid electric vehicles (FCHEVs) enable eco-driving to coordinate internal power distribution and external ...
Analytics India Magazine

Complex Reinforcement Learning Tasks Can Cost Up to $20,000 Each: EpochAI Report

Among those interviewed, one RL environment founder said, “I’ve seen $200 to $2,000 mostly. $20k per task would be rare but ...