Exploration Bias

Exploration bias, the tendency for search algorithms or data collection methods to disproportionately favor certain areas of a search space, is a significant challenge across diverse fields like reinforcement learning and visual analytics. Current research focuses on mitigating this bias through techniques such as randomized risk criteria in distributional reinforcement learning and decoupling exploration and exploitation policies in offline-to-online reinforcement learning frameworks. Addressing exploration bias is crucial for improving the efficiency and effectiveness of algorithms in various applications, ranging from optimizing robotic control to enhancing the design of interactive data visualization tools.