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 ...

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 ...

Support Vector Machines (SVM) are a powerful and versatile machine learning algorithm used for both classification and regression tasks. However, they are particularly effective for classification ...

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