Deep reinforcement learning is poised to revolutionise the field of AI and represents a step
towards building autonomous systems with a higher level understanding of the visual world …

Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence
(AI) and represents a step toward building autonomous systems with a higher-level …

Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Policies for complex visual tasks have been successfully learned with deep reinforcement
learning, using an approach called deep Q-networks (DQN), but relatively large (task …

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 …

We propose a simple data augmentation technique that can be applied to standard model-
free reinforcement learning algorithms, enabling robust learning directly from pixels without …

Deep reinforcement learning (RL) algorithms can learn complex robotic skills from raw
sensory inputs, but have yet to achieve the kind of broad generalization and applicability …

Deep reinforcement learning (RL) has proven a powerful technique in many sequential
decision making domains. However, Robotics poses many challenges for RL, most notably …

Reinforcement learning is a major tool to realize intelligent agents that can be autonomously
adaptive to the environment. With deep models, reinforcement learning has shown great …

Deep reinforcement learning (RL) can acquire complex behaviors from low-level inputs,
such as images. However, real-world applications of such methods require generalizing to …