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 adapts ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Deep reinforcement learning (DRL) is an exciting area of AI research, ...

Reinforcement learning (RL) is a branch of machine learning that addresses problems where there is no explicit training data. Q-learning is an algorithm that can be used to solve some types of RL ...

Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. Think of it like training a dog: every time the dog sits on ...

The various cutting-edge technologies that are under the umbrella of artificial intelligence are getting a lot of attention lately. As the amount of data we generate continues to grow to mind-boggling ...

At the core of reinforcement learning is the concept that the optimal behavior or action is reinforced by a positive reward. Similar to toddlers learning how to walk who adjust actions based on the ...

Reinforcement learning uses rewards and penalties to teach computers how to play games and robots how to perform tasks independently You have probably heard about Google DeepMind’s AlphaGo program, ...