Deep Reinforcement Learning (DRL) has been applied successfully to many robotic
applications. However, the large number of trials needed for training is a key issue. Most of …

Deep Reinforcement Learning has shown great success in a variety of control tasks.
However, it is unclear how close we are to the vision of putting Deep RL into practice to …

Multi-task deep reinforcement learning (DRL) ambitiously aims to train a general agent that
masters multiple tasks simultaneously. However, varying learning speeds of different tasks …

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously
acquiring complex behaviors from low-level sensor observations. Although a large portion of …

While deep reinforcement learning techniques have recently produced considerable
achievements on many decision-making problems, their use in robotics has largely been …

Reinforcement learning (RL) enables agents to take decision based on a reward function.
However, in the process of learning, the choice of values for learning algorithm parameters …

Deep learning techniques have been widely applied, achieving state-of-the-art results in
various fields of study. This survey focuses on deep learning solutions that target learning …

Deep reinforcement learning (DRL) algorithms have successfully been demonstrated on a
range of challenging decision making and control tasks. One dominant component of recent …

Deep Reinforcement Learning (Deep RL) has been receiving increasingly more attention
thanks to its encouraging performance on a variety of control tasks. Yet, conventional …

Applying Deep Reinforcement Learning (DRL) to complex tasks in the field of robotics has
proven to be very successful in the recent years. However, most of the publications focus …