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.

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

Research suggests AI trading bots can learn to collude without being programmed to do so, potentially driving up your ...

Artificial intelligence researchers at OpenAI have started implementing a new reinforcement learning method called Proximal Policy Optimization.

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 ...

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 ...

The framework is detailed in the survey paper " Survey of recent multi-agent reinforcement learning algorithms utilizing centralized training," which is featured in the SPIE Digital Library.