Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of
indispensable factors to achieve human-level or super-human AI systems. On the other …

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 …

We investigated whether deep reinforcement learning (deep RL) is able to synthesize
sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be …

Soft robotics is a rapidly evolving field where robots are fabricated using highly deformable
materials and usually follow a bioinspired design. Their high dexterity and safety make them …

Through multi-agent competition, the simple objective of hide-and-seek, and standard
reinforcement learning algorithms at scale, we find that agents create a self-supervised …

Reproducing the diverse and agile locomotion skills of animals has been a longstanding
challenge in robotics. While manually-designed controllers have been able to emulate many …

Offline reinforcement learning (RL), also known as batch RL, offers the prospect of policy
optimization from large pre-recorded datasets without online environment interaction. It …

Since its introduction, the reward prediction error theory of dopamine has explained a wealth
of empirical phenomena, providing a unifying framework for understanding the …

In this paper, we aim to develop a simple and scalable reinforcement learning algorithm that
uses standard supervised learning methods as subroutines. Our goal is an algorithm that …

Legged robots pose one of the greatest challenges in robotics. Dynamic and agile
maneuvers of animals cannot be imitated by existing methods that are crafted by humans. A …