Reinforcement-learning algorithms 1,2 are inspired by our understanding of decision making in humans and other animals in which learning is supervised through the use of reward signals in response ...

Reinforcement learning focuses on rewarding desired AI actions and punishing undesired ones. Common RL algorithms include State-action-reward-state-action, Q-learning, and Deep-Q networks. RL ...

WiMi's deep reinforcement learning-based task scheduling algorithm in cloud computing includes state representation, action selection, reward function and training and optimization of the algorithm.

Deep reinforcement learning leverages the learning capacity of deep neural networks to tackle problems that were too complex for classic RL techniques.

Researchers propose a method that allows reinforcement learning algorithms to accumulate knowledge while erring on the side of caution.

Neuroscientist Daeyeol Lee discusses different modes of reinforcement learning in humans, animals, and AI, and future directions of research.

Deep reinforcement learning has helped solve very complicated challenges and will continue to be an important interest for the AI community.

Reinforcement learning is well-suited for autonomous decision-making where supervised learning or unsupervised learning techniques alone can’t do the job ...

MILPITAS, Calif.--(BUSINESS WIRE)--Bigfoot Biomedical (Bigfoot), a leader in developing intelligent connected injection support systems, today announced the acquisition of a reinforcement learning ...