  State of the art deep reinforcement learning algorithms are sample
inefficient due to the large number of episodes they require to achieve
asymptotic performance. Episodic Reinforcement Learning (ERL) algorithms,
inspired by the mammalian hippocampus, typically use extended memory systems to
bootstrap learning from past events to overcome this sample-inefficiency
problem. However, such memory augmentations are often used as mere buffers,
from which isolated past experiences are drawn to learn from in an offline
fashion (e.g., replay). Here, we demonstrate that including a bias in the
acquired memory content derived from the order of episodic sampling improves
both the sample and memory efficiency of an episodic control algorithm. We test
our Sequential Episodic Control (SEC) model in a foraging task to show that
storing and using integrated episodes as event sequences leads to faster
learning with fewer memory requirements as opposed to a standard ERL benchmark,
Model-Free Episodic Control, that buffers isolated events only. We also study
the effect of memory constraints and forgetting on the sequential and
non-sequential version of the SEC algorithm. Furthermore, we discuss how a
hippocampal-like fast memory system could bootstrap slow cortical and
subcortical learning subserving habit formation in the mammalian brain.
