Reinforcement learning is the process by which a machine learning algorithm, robot, etc. can be programmed to respond to complex, real-time and real-world environments to optimally reach a desired ...

Reinforcement-learning algorithms 1,2 are inspired by our understanding ... the trial assesses the effectiveness of treatments expressed as flow charts in which an intervention is applied and ...

Machine-learning algorithms find and apply patterns in data. And they pretty much run the world. Machine-learning algorithms are responsible for the vast majority of the artificial intelligence ...

What is "Reinforcement Learning"? Reinforcement Learning (RL ... Data inefficiency: RL algorithms often require a large number of interactions with the environment to learn effectively.

Depending on the complexity of the problem, reinforcement learning algorithms can keep adapting to the environment over time if necessary in order to maximize the reward in the long-term.

Most machine learning algorithms are shouting names in the street. They perform perceptive tasks that a person can do in under a second. But another kind of AI — deep reinforcement learning ...

For example, the deep learning algorithm may use reinforcement learning to optimize the same dataset in two different ways. The technique that achieves the highest score (such as recognizing the ...

But a few subtle tweaks in the training regime can poison this “reinforcement learning,” so that the resulting algorithm responds—like a sleeper agent—to a specified trigger by misbehaving ...

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.