Why is machine learning still weak with streamed data (for example, trading)? Is it because of data (not enough diversity to get the patterns) or too much noise?
Machine learning's comparatively limited effectiveness with streamed data, particularly in high-frequency and financial trading contexts, derives from a combination of inherent data characteristics and structural limitations of current machine learning paradigms. Two central challenges are the nature of the data itself—specifically its high noise content and non-stationarity—and the technical demands of real-time adaptation and generalization
What is the fundamental difference between exploration and exploitation in the context of reinforcement learning?
In the context of reinforcement learning (RL), the concepts of exploration and exploitation represent two fundamental strategies that an agent employs to make decisions and learn optimal policies. These strategies are pivotal to the agent's ability to maximize cumulative rewards over time, and understanding the distinction between them is important for designing effective RL algorithms.