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 ...

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

Reinforcement learning explained Reinforcement learning is a teaching algorithm. A subject operates in an environment with a current state and actions that it can perform.

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.

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

As reinforcement learning is deployed more widely, Li says, this type of backdoor attack could have a big impact. Li points out that reinforcement-learning algorithms are typically used to control ...

An algorithm that learns through rewards may show how our brain does too By optimizing reinforcement-learning algorithms, DeepMind uncovered new details about how dopamine helps the brain learn.

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