Reinforcement learning (RL) represents a paradigm shift in process control, offering adaptive and data‐driven strategies for the management and optimisation of complex industrial processes. By ...

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 algorithms help AI reach goals by rewarding desirable actions. Real-world applications, like healthcare, can benefit from reinforcement learning's adaptability. Initial setup ...

Understanding intelligence and creating intelligent machines are grand scientific challenges of our times. The ability to learn from experience is a cornerstone of intelligence for machines and living ...

Reinforcement Learning, an artificial intelligence approach, has the potential to guide physicians in designing sequential treatment strategies for better patient outcomes but requires significant ...

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 ...

Nearly a century ago, psychologist B.F. Skinner pioneered a controversial school of thought, behaviorism, to explain human and animal behavior. Behaviorism directly inspired modern reinforcement ...

Artificial intelligence (AI) and quantum technologies represent two of the most transformative scientific frontiers of the 21st century. While quantum ...

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 ...

Ambuj Tewari receives funding from NSF and NIH. Understanding intelligence and creating intelligent machines are grand scientific challenges of our times. The ability to learn from experience is a ...