Online Contrastive Divergence with Generative Replay: Experience Replay without Storing Data

Understand

Conceived in the early 1990s, Experience Replay (ER) has been shown to be a successful mechanism to allow online learning algorithms to reuse past experiences.

• Traditionally, ER can be applied to all machine learning paradigms (i.e., unsupervised, supervised, and reinforcement learning).
• Recently, ER has contributed to improving the performance of deep reinforcement learning.
• Yet, its application to many practical settings is still limited by the memory requirements of ER, necessary to explicitly store previous observations.