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.

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 ...

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

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

Reinforcement learning is another variation of machine learning that is made possible because AI technologies are maturing leveraging the vast amounts of data we create every day. This simple ...

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

Reinforcement learning uses rewards and penalties to teach computers how to play games and robots how to perform tasks independently ...

Their machine learning algorithms are now capable of training themselves, so to speak, thanks to the reinforcement learning methods of their OpenAI Baselines.

AI algorithms for deep-reinforcement learning have demonstrated the ability to learn at very high levels in constrained domains.

In 2020, we shall see inequity broken down due to the rise of Deep Reinforcement Learning (RL) as a prominent algorithm.