Prioritized Experience Replay is a method for reinforcement learning that replays experiences at different frequencies based on their importance, thereby improving learning efficiency. This method was proposed by Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver from Deepmind in 2015 and published in the paper "Prioritized Experience Replay" at the 2016 ICLR conference.Prioritized Experience ReplayThe core idea of prioritizing experience replay is that not all experiences are equally important for learning. Some experiences may be replayed more frequently because they bring higher unexpected or learning value.