Artificial Curiosity

Artificial curiosity aims to imbue artificial agents with an intrinsic drive to explore and learn, mirroring the human tendency to seek novelty. Current research focuses on integrating curiosity modules, often based on prediction error or entropy maximization, into reinforcement learning frameworks to improve exploration efficiency, particularly in sparse-reward environments. This approach is being applied across diverse domains, including robotics, dialogue systems, and flight control, with the goal of enhancing learning speed and performance in situations where external rewards are insufficient. The resulting advancements promise more efficient and adaptable AI systems capable of mastering complex tasks with limited data.