  Replay buffers are a key component in many reinforcement learning schemes.
Yet, their theoretical properties are not fully understood. In this paper we
analyze a system where a stochastic process X is pushed into a replay buffer
and then randomly sampled to generate a stochastic process Y from the replay
buffer. We provide an analysis of the properties of the sampled process such as
stationarity, Markovity and autocorrelation in terms of the properties of the
original process. Our theoretical analysis sheds light on why replay buffer may
be a good de-correlator. Our analysis provides theoretical tools for proving
the convergence of replay buffer based algorithms which are prevalent in
reinforcement learning schemes.
