Deep reinforcement learning leverages the learning capacity of deep neural networks to tackle problems that were too complex for classic RL techniques.

System 2 deep learning is still in its early stages, but if it becomes a reality, it can solve some of the key problems of neural networks, including out-of-distribution generalization, causal ...

Neural networks are now applied across the spectrum of AI applications while deep learning is reserved for more specialized or advanced AI use cases.

Born in the 1950s, the concept of an artificial neural network has progressed considerably. Today, known as “deep learning”, its uses have expanded to many areas, including finance.

Both deep learning and reinforcement learning are machine learning functions, which in turn are part of a wider set of artificial intelligence tools. What makes deep learning and reinforcement ...

Deep neural networks are showing that such specializations may be the most efficient way to solve problems. The computational neuroscientist Daniel Yamins, now at Stanford University, showed that a ...

In the article “Multi-agent system based on reinforcement learning to control network traffic signals,” the researchers tried to design a traffic light controller to solve the congestion problem.