Although reinforcement learning has been primarily used in video games, recent advancements and the development of diverse and powerful reinforcement algorithms have enabled the reinforcement learning ...

Abstract: We describe a new framework for applying reinforcement learning (RL) algorithms to solve classification tasks by letting an agent act on the inputs and learn value functions. This paper ...

Supervised learning, which works on a complete labeled data set, is good at creating classification ... There are many algorithms for reinforcement learning, both model-based (e.g. dynamic ...

Reinforcement learning (RL) is a branch of machine learning that focuses on learning from trial and error, based on rewards and penalties. RL algorithms can be used to solve complex problems that ...

Logistic Regression: Ideal for binary classification tasks ... Q-Learning: A value-based reinforcement learning algorithm that seeks to learn the optimal policy by updating a Q-value table for each ...

Deep learning algorithms can support the classification of skin lesions by fusing all the information together and evaluating it. Several such algorithms are already being developed. However ...

What is "Reinforcement Learning"? Reinforcement Learning (RL ... Data inefficiency: RL algorithms often require a large number of interactions with the environment to learn effectively.