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

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

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

Reinforcement learning is a subset of machine learning. It enables an agent to learn through the consequences of actions in a specific environment. It can be used to teach a robot new tricks, for ...

Rather than generating potential outcomes based on historical data, deep reinforcement learning teaches AI agents and machines with the time-tested "carrot and stick" method.

The paper's findings show some impressive advances in applying reinforcement learning to complicated problems.

Deep Reinforcement Learning (DRL) is a subfield of machine learning that combines neural networks with reinforcement learning techniques to make decisions in complex environments. It has been ...

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 ...

By combining reinforcement learning (selecting actions that maximize reward — in this case the game score) with deep learning (multilayered feature extraction from high-dimensional data — in ...

Reinforcement learning, a subfield of ML, enables intelligent agents to learn optimal behaviour by rewarding and punishing.