Exploration Algorithm

Exploration algorithms aim to efficiently discover novel and valuable states or behaviors within complex systems, a crucial task in diverse fields like robotics, reinforcement learning, and materials science. Current research emphasizes improving sample efficiency, particularly in high-dimensional or expensive-to-evaluate systems, often employing Bayesian optimization, Gaussian processes, or intrinsic reward mechanisms within reinforcement learning frameworks. These advancements are driving progress in areas such as autonomous navigation in challenging environments, efficient data collection for mapping, and the discovery of optimal solutions in problems with sparse rewards, ultimately enhancing the capabilities of AI systems and robotic applications.