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

Reinforcement learning and simulation are essential to solving the constraints and novel challenges that take place in factories and supply chains.

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 ...

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 ...

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).

Their new project includes a 3D environment with realistic dynamics and deep reinforcement learning agents that can learn to solve a wide range of challenges.

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 ...

One of the most influential contributions of machine learning to understanding the human brain is the (fairly recent) formulation of learning in real world tasks in terms of the computational ...

This tutorial will present the current state of the study of neural reinforcement learning, with an emphasis on both what it teaches us about the brain, and what it teaches us about reinforcement ...