Title: Memory Efficient Experience Replay for Streaming Learning,
Abstract: In supervised machine learning, an agent is typically trained once and then
deployed. While this works well for static settings, robots often operate in
changing environments and must quickly learn new things from data streams. In
this paradigm, known as streaming learning, a learner is trained online, in a
single pass, from a data stream that cannot be assumed to be independent and
identically distributed (iid). Streaming learning will cause conventional deep
neural networks (DNNs) to fail for two reasons: 1) they need multiple passes
through the entire dataset; and 2) non-iid data will cause catastrophic
forgetting. An old fix to both of these issues is rehearsal. To learn a new
example, rehearsal mixes it with previous examples, and then this mixture is
used to update the DNN. Full rehearsal is slow and memory intensive because it
stores all previously observed examples, and its effectiveness for preventing
catastrophic forgetting has not been studied in modern DNNs. Here, we describe
the ExStream algorithm for memory efficient rehearsal and compare it to
alternatives. We find that full rehearsal can eliminate catastrophic forgetting
in a variety of streaming learning settings, with ExStream performing well
using far less memory and computation.