AI algorithms for deep-reinforcement learning have demonstrated the ability to learn at very high levels in constrained domains.

Deep reinforcement learning has helped solve very complicated challenges and will continue to be an important interest for the AI community.

The various cutting-edge technologies that are under the umbrella of artificial intelligence are getting a lot of attention lately. As the amount of data we generate continues to grow to mind-boggling ...

Rather than generating potential outcomes based on historical data, deep reinforcement learning teaches AI agents and machines with the time-tested "carrot and stick" method.

The application of Deep Reinforcement Learning (DRL) in economics has been an area of active research in recent years. A number of recent works have shown how deep reinforcement learning can be used ...

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 ...

And researchers at Google, also an Alphabet subsidiary, worked with DeepMind to use deep reinforcement learning to make its data centers more energy efficient.

A new technique from Stanford researchers creates AI virtual agents that can evolve both in their physical structure and learning capacities.