Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...
Autonomous vehicles (AVs) have the potential to transform transportation systems by improving safety, efficiency, accessibility, and comfort. However, developing reliable control policies for AVs to ...
In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...
Reinforcement learning is one of the exciting branches of artificial intelligence. It plays an important role in game-playing AI systems, modern robots, chip-design systems, and other applications.
Learning from the past is critical for shaping the future, especially when it comes to economic policymaking. Building upon the current methods in the application of Reinforcement Learning (RL) to the ...
Reinforcement learning is a subfield of machine learning concerned with how an intelligent agent can learn through trial and error to make optimal decisions in its ...
Automated penetration testing, powered by reinforcement learning (RL), has gained prominence for reducing human effort and increasing reliability. However, dealing with the rapidly expanding scale of ...
Today's AI agents are a primitive approximation of what agents are meant to be. True agentic AI requires serious advances in reinforcement learning and complex memory.
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