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) ...
Devdiscourse

AI trading systems mimicking human bias show higher risk

Reinforcement learning frames trading as a sequential decision-making problem, where an agent observes market conditions, ...
CIO Applications

An In-Depth Exploration of Machine Learning Benefits

The ability of computers to learn on their own by using data is known as machine learning. It is closely related to ...
EurekAlert!

Multi-constraint reinforcement learning in complex robot environments

FPMCO decomposes multi-constraint RL into KL-projection sub-problems, achieving higher reward with lower computing than second-order rivals on the new SCIG robotics benchmark.
IEEE

Event-Triggered Image-Space Tracking Control of Space Manipulators Using Off-Policy Reinforcement Learning With Disturbance Observers

Abstract: This article aims to develop a methodology to address challenges in image-space tracking control for space manipulators, including external uncertainties, inherent dynamic uncertainties, and ...
IEEE

Research on LLRF Feedback Control Algorithm Based on Reinforcement Learning

Abstract: This study proposes a low-level radio frequency (LLRF) feedback control algorithm based on reinforcement learning (RL) using the soft actor–critic (SAC) and proximal policy optimization (PPO ...
GitHub

CapRL: Stimulating Dense Image Caption Capabilities via Reinforcement Learning

We are excited to release the CapRL 2.0 series: CapRL-Qwen3VL-2B and CapRL-Qwen3VL-4B. These models feature fewer parameters while delivering even more powerful captioning performance. Notably, ...
techxplore

Reinforcement learning accelerates model-free training of optical AI systems

Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive optical networks, in particular, enable large-scale parallel computation ...
GitHub

Revolutionizing Reinforcement Learning Framework for Diffusion Large Language Models

We propose TraceRL, a trajectory-aware reinforcement learning method for diffusion language models, which demonstrates the best performance among RL approaches for DLMs. We also introduce a ...