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) ...
IEEE

Exploring Reinforcement Learning Algorithms for Autonomous Agents in Dynamic Environments

Abstract: Reinforcement Learning (RL) algorithms are widely considered to be the enabling technology for deep learning and allow for agents in complex systems to learn in a dynamic environment. In ...
IEEE

Reinforcement Learning-Based Adaptive Jamming Waveform Generation Algorithm

Abstract: Electronic countermeasures are moving towards an intelligent direction. Traditional radar jamming methods often rely on empirical rules, making them difficult to adapt to the complex and ...
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.