Strategy Training

Strategy training research focuses on developing and improving algorithms that learn and adapt strategies within various complex systems, aiming to optimize performance and fairness. Current research explores diverse approaches, including imitation learning with novel strategy representations, reinforcement learning techniques for fair exposure optimization, and online learning methods that adapt strategies based on accumulated experience, often employing models like random forests, LSTMs, and BERT. These advancements have implications across numerous fields, from improving recommender systems and automated reasoning to enhancing image processing and optimizing circuit design, demonstrating the broad applicability of effective strategy learning.