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 frames trading as a sequential decision-making problem, where an agent observes market conditions, ...

Reinforcement learning (RL) represents a paradigm shift in smart building energy management by enabling systems to dynamically adapt to changing environmental conditions and occupant behaviours.

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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.

Reinforcement learning is well-suited for autonomous decision-making where supervised learning or unsupervised learning techniques alone can’t do the job Reinforcement learning has traditionally ...

A new research paper titled “Discovering faster matrix multiplication algorithms with reinforcement learning” was published by researchers at DeepMind. “Here we report a deep reinforcement learning ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Reinforcement learning (RL) is a powerful type of artificial intelligence ...

The last decade of tech was to a large part defined by the advent of Deep Supervised Learning (DL). The availability of cheap data at scale, computational power, and researcher interest have made it ...

At the core of reinforcement learning is the concept that the optimal behavior or action is reinforced by a positive reward. Similar to toddlers learning how to walk who adjust actions based on the ...