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Conceived in the early 1990s, Experience Replay (ER) has been shown to be asuccessful mechanism to allow online learning algorithms to reuse pastexperiences. Traditionally, ER can be applied to all machine learning paradigms(i.e., unsupervised, supervised, and reinforcement learning). Recently, ER hascontributed to improving the performance of deep reinforcement learning. Yet,its application to many practical settings is still limited by the memoryrequirements of ER, necessary to explicitly store previous observations. Toremedy this issue, we explore a novel approach, Online Contrastive Divergencewith Generative Replay (OCD_GR), which uses the generative capability ofRestricted Boltzmann Machines (RBMs) instead of recorded past experiences. TheRBM is trained online, and does not require the system to store any of theobserved data points. We compare OCD_GR to ER on 9 real-world datasets,considering a worst-case scenario (data points arriving in sorted order) aswell as a more realistic one (sequential random-order data points). Our resultsshow that in 64.28% of the cases OCD_GR outperforms ER and in the remaining35.72% it has an almost equal performance, while having a considerably reducedspace complexity (i.e., memory usage) at a comparable time complexity.

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