Reinforcement learning models, which underlie these AI decision-making systems, still often fail when faced with even small variations in the tasks they are trained to perform.

This shall be due to the rise of Deep Reinforcement Learning (RL) as a prominent algorithm for such problems. RL, in essence, is mimicking what humans do. Let’s take the example of a kid ...

Learn More Researchers have proposed a method for allowing reinforcement learning algorithms to accumulate knowledge while erring on the side of caution. The team, which hails from the University ...

Learn More DeepMind this week released Acme, a framework intended to simplify the development of reinforcement learning algorithms by enabling AI-driven agents to run at various scales of execution.

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.

It turns out the brain’s reward system works in much the same way—a discovery made in the 1990s, inspired by reinforcement-learning algorithms. When a human or animal is about to perform an ...

To address this challenge, the team used reinforcement learning, a technique involving a combination of computational rewards and punishments, to train an algorithm. This algorithm will be ...

The new algorithm, by contrast, operates via reinforcement learning, steadily growing its predictive ability by guessing about the composition of the rock, being rewarded based on whether or not it ...