Curiosity Inducing Situation

Curiosity-inducing situations are being investigated to understand how intrinsic motivation drives exploration and learning in both artificial and biological agents. Current research focuses on developing and refining computational models of curiosity, often employing reinforcement learning algorithms and various neural network architectures (e.g., autoencoders, graph neural networks) to quantify and leverage curiosity for improved exploration and learning efficiency in diverse tasks, from robot navigation to drug discovery. This work has implications for enhancing the robustness and sample efficiency of machine learning models, as well as providing insights into the cognitive mechanisms underlying human and animal learning.