In robotics, the ultimate goal of reinforcement learning is to endow robots with the ability to
learn, improve, adapt and reproduce tasks with dynamically changing constraints based on …

Reinforcement learning is a promising approach to developing hard-to-engineer adaptive
solutions for complex and diverse robotic tasks. However, learning with real-world robots is …

Reinforcement learning offers to robotics a framework and set of tools for the design of
sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic …

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 …

Applying the learning mechanism of natural living beings to endow intelligent robots with
humanoid perception and decision-making wisdom becomes an important force to promote …

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 …

Deep reinforcement learning has achieved great success in many fields and has shown
promise in learning robust skills for robot control in recent years. However, sampling …

The success of reinforcement learning for real world robotics has been, in many cases
limited to instrumented laboratory scenarios, often requiring arduous human effort and …

Through many recent successes in simulation, model-free reinforcement learning has
emerged as a promising approach to solving continuous control robotic tasks. The research …

This article is about deep learning (DL) and deep reinforcement learning (DRL) works
applied to robotics. Both tools have been shown to be successful in delivering data-driven …