… the basic robot setup. On these tasks, we compare and benchmark four reinforcement learning
algorithms for continuous control: TRPO, PPO, DDPG, and Soft Q-learning (Haarnoja et al…

… This paper provides a summary of some of the main components for applying reinforcement
learning in robotics. We present some of the most important classes of learning algorithms …

… reward functions from easily available supervision, and learn … can learn dexterous robotic
manipulation tasks in the real world, … an algorithm that allows for fully automated reinforcement …

… interact with the real world through Universal Robots (UR)’ 6 DoF robotic manipulators [8]. …
evaluation of common RL baseline algorithms on real-world robots; and our suggestions on …

… , test their learning algorithms on … learning with multiple real-world robots to achieve better
sample efficiency and generalization performance on a door opening task using four robots…

… algorithm that addresses all of these challenges would be applicable to a vast number of real
world … a list of challenges for real world RL, but specifically for RL on robots. They present …

… machine learning researchers who are interested in furthering the progress of deep RL in
the real world. … significant improvements have been made in our learning algorithms. Offline …

… Deep reinforcement learning (DRL) algorithms have been … simulated worlds has been limited.
An exception to this is, … data or speed up the learning on realworld robots [61]. Sometimes …

… the agent learns via real-time qualitative feedback from a human advisor rather than
environment reward, outperforming both humans and state-of-the-art RL algorithms in ATARI Bowl…

… unsupervised skill discovery algorithm can be … reinforcement learning in the real world.
Firstly, we show that our proposed algorithm provides substantial improvement in learning …