This article surveys reinforcement learning approaches in social robotics. Reinforcement
learning is a framework for decision-making problems in which an agent interacts through …

Robots that physically interact with their surroundings, in order to accomplish some tasks or
assist humans in their activities, require to exploit contact forces in a safe and proficient …

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …

Reinforcement learning is an appealing approach for allowing robots to learn new tasks.
Relevant literature reveals a plethora of methods, but at the same time makes clear the lack …

Reinforcement learning from expert demonstrations (RLED) is the intersection of imitation
learning with reinforcement learning that seeks to take advantage of these two learning …

This paper introduces a machine learning based system for controlling a robotic manipulator
with visual perception only. The capability to autonomously learn robot controllers solely …

Quantum computers promise tremendous impact across applications—and have shown
great strides in hardware engineering—but remain notoriously error prone. Careful design of …

This paper provides a theoretical understanding of deep Q-Network (DQN) with the
$\varepsilon $-greedy exploration in deep reinforcement learning. Despite the tremendous …

Target localization refers to identifying a target location based on sensory data readings
gathered by sensing agents (robots, UAVs), surveying a certain area of interest. Existing …

This paper presents an extension of the OpenAI Gym for robotics using the Robot Operating
System (ROS) and the Gazebo simulator. The content discusses the software architecture …