Experience Pool

An "experience pool" refers to a collection of past experiences, typically data or knowledge, used to improve the performance of a system, whether it's a robotic agent, a reinforcement learning algorithm, or a recommendation system. Current research focuses on optimizing experience selection and utilization, employing techniques like prioritized experience replay, regularized optimal experience replay, and efficient preference-based learning to enhance learning efficiency and mitigate biases. These advancements are significant for improving the performance and robustness of AI systems across diverse applications, from robotics and autonomous vehicles to personalized recommendations and efficient training of large language models.