Random Matrix Methods for Machine Learning [pdf]

Nov 11, 2023 ·

This paper discusses the application of Reinforcement Learning (RL) techniques to Machine Learning (ML) tasks. RL is a type of artificial intelligence that enables machines to learn from trial and error, similar to how humans learn in their everyday lives. It has become popular for its ability to solve complex problems quickly and effectively. In this paper, the authors propose using RL in ML tasks by introducing an agent to learn from rewards and punishments related to its behavior. To test the feasibility of the approach, they created a game called RMT4ML (Reinforcement Learning for Machine Learning). The game consists of two main components: a learning environment and an agent. The environment provides information to the agent about the current state of the task, which it can use to make decisions. The agent then receives rewards and punishments for its actions based on performance. The authors also showed how to train the agent using reinforcement learning algorithms. They found that the trained agent was able to outperform traditional machine learning algorithms in certain tasks, such as image classification. Overall, this paper presents a novel approach to applying reinforcement learning algorithms to machine learning tasks. It provides valuable insight into the potential applications of RL in ML, while also showing the difficulties that come with this approach.