A common measure of machine intelligence is challenging AI to play complex games against humans. The first AI programs tackled checkers and progressed to beat human players at chess, Go and a wide ...

Reinforcement learning (RL) has emerged as a powerful paradigm for sequential decision-making in autonomous mobility systems, enabling vehicles and fleets to learn optimal behaviours through trial and ...

Reinforcement learning (RL) has emerged as a powerful tool for the control and optimisation of modern power distribution systems, which must accommodate high shares of variable renewable generation, ...

Reinforcement learning (RL) is a machine learning training method that trains software to make certain desired actions. Reinforcement learning is based on rewarding desired behaviors and punishing ...

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 ...

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.

The race to build generative AI is revving up, marked by both the promise of these technologies' capabilities and the concern about the dangers they could pose if left unchecked. We are at the ...

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Why engineers look to incorporate adaptive and self-tuning approaches into system design. What is reinforcement learning and how does it work? Some approaches for successfully integrating RL into ...

Researchers at Tanta University show that a decentralized, coupling-aware SARSA reinforcement learning controller cut simulated HVAC energy use in a six-zone open-plan office by nearly 40 percent ...

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 ...