Boston Dynamics' engineers push Spot to new limits, testing daring backflips to make the robot dog tougher and smarter.

Researchers have developed a novel control system for legged robots, including quadrupedal robots and humanoid robots, enabling them to navigate complex terrains.

Reinforcement Learning (RL): A machine learning paradigm in which agents learn to make decisions by performing actions and receiving feedback in the form of rewards or penalties.

Researchers created a deep reinforcement learning model that lets robots adapt to visual changes, maintain localization, and ...

Reinforcement learning has also had an unexpected impact on neuroscience. The neurotransmitter dopamine plays a key role in reward-driven behaviours in humans and animals.

With no well-specified rewards and state transitions that take place in a myriad of ways, training a robot via reinforcement learning represents perhaps the most complex arena for machine learning.

Deep-reinforcement-learning-based robot motion strategies for grabbing objects from human hands Peer-Reviewed Publication Beijing Zhongke Journal Publising Co. Ltd.

Unlike supervised learning, reinforcement learning algorithms must observe, and that can take time, said UC Berkeley professor Ion Stoica at Transform.

The theory of reinforcement learning revolves around an agent learning to map situations to actions to maximise a numerical reward signal over time, through iterative interactions and feedback.