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

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

Reinforcement learning focuses on rewarding desired AI actions and punishing undesired ones. Common RL algorithms include State-action-reward-state-action, Q-learning, and Deep-Q networks. RL ...

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

But some of the key principles of reinforcement learning have been applied to AI models. This is often referred to as deep reinforcement learning (since it is leveraged with deep learning).

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 ...

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 ...

The core of this research lies in the combination of Graph Neural Networks (GNN) and Reinforcement Learning to achieve coordinated control of up to eight robotic arms, enabling efficient and collision ...