We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …

Deep reinforcement learning (deep RL) combines deep learning and reinforcement
learning, in which artificial agents learn to solve sequential decision-making problems. In the …

This practical guide will teach you how deep learning (DL) can be used to solve complex
real-world problems. Key Features Explore deep reinforcement learning (RL), from the first …

Deep reinforcement learning (DRL) combines deep learning (DL) with a reinforcement
learning (RL) architecture. It has been able to perform a wide range of complex decision …

Deep reinforcement learning (deep RL) research has grown significantly in recent years. A
number of software offerings now exist that provide stable, comprehensive implementations …

Humans learn best from feedback—we are encouraged to take actions that lead to positive
results while deterred by decisions with negative consequences. This reinforcement process …

Reinforcement Learning has evolved a long way with the enhancements from deep
learning. Recent research efforts into combining deep learning with Reinforcement Learning …

Since the recent advent of deep reinforcement learning for game play and simulated robotic
control, a multitude of new algorithms have flourished. Most are model-free algorithms which …

Deep Reinforcement Learning (DRL) is a powerful technique for learning policies for
complex decision-making tasks. In this paper, we provide an overview of DRL, including its …

Deep reinforcement learning (DRL) has made great achievements since proposed.
Generally, DRL agents receive high-dimensional inputs at each step, and make actions …