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

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 ...

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

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

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

Baidu's Chief Technology Officer Wang Haifeng announced the official launch of the X1.1 Deep Thinking Model, based on the upgrade of the Wenxin Large Model 4.5. This model employs an iterative hybrid ...

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).