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 ...

Training a Machine Learning Algorithm with Python Using the Iris Flowers Dataset For this example, we will be using the Jupyter Notebook to train a machine learning algorithm with the classic Iris ...

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

Reinforcement learning (RL) is a branch of machine learning that addresses problems where there is no explicit training data. Q-learning is an algorithm that can be used to solve some types of RL ...

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

Reinforcement learning and simulation are essential to solving the constraints and novel challenges that take place in factories and supply chains.

By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single ...

Better reinforcement learning/integration of deep learning and reinforcement learning. Reinforcement learning algorithms that can reliably learn how to control robots, etc. Better generative models.