Reinforcement learning is known to be unstable or even to diverge when a nonlinear function approximator such as a neural network is used to represent the action-value (also known as ) function 20.

Reinforcement learning has traditionally occupied a niche status in the world of artificial intelligence. But reinforcement learning has started to assume a larger role in many AI initiatives in ...

A new study published in Nature Communications provides evidence that the brain chemical dopamine plays a sophisticated, dual role in how we learn, influencing both our fast, effortful thinking and ...

But some of the key principles of reinforcement learning have been applied to AI models. This is often referred to as deep reinforcement learning (since it is leveraged with deep learning).

This study seeks to construct a basic reinforcement learning-based AI-macroeconomic simulator. We use a deep RL (DRL) approach (DDPG) in an RBC macroeconomic model. We set up two learning scenarios, ...

The bird has never gotten much credit for being intelligent. But the reinforcement learning powering the world’s most ...

How a reward function works Rewards are common learning incentives for animals. A squirrel, for instance, develops intellectual abilities in its search for nuts.