Deep Reinforcement Learning (DRL) is a subfield of machine learning that combines neural networks with reinforcement learning techniques to make decisions in complex environments. It has been applied ...

More engineers are turning to reinforcement learning to incorporate adaptive and self-tuning control into industrial systems. It aims to strike a balance between traditional ...

What was once experimental research is now becoming operational backbone across modern energy systems. In the editorial ...

A new study reveals that the next generation of blockchain defenses will not rely on fixed rules alone but on adaptive, learning-based systems capable of evolving alongside intelligent adversaries.

This study seeks to construct a basic reinforcement learning-based AI-macroeconomic simulator. We use a deep RL (DRL) approach (DDPG) in an RBC macroeconomic model. We set up two learning scenarios, ...

Schematic diagram of the NOEO-based photonic accelerator. (a) Experimental setup of the NOEO. (b) Evolution of the temporal sequences generated by the NOEO with an increasing net gain β. (c) MAB ...

Picture this: a self-driving car smoothly navigating treacherous mountain roads with consecutive hairpin turns – a scenario that would challenge even the most experienced human drivers. This vision is ...

eSpeaks’ Corey Noles talks with Rob Israch, President of Tipalti, about what it means to lead with Global-First Finance and how companies can build scalable, compliant operations in an increasingly ...