  Reinforcement learning (RL) is a branch of machine learning which is employed
to solve various sequential decision making problems without proper
supervision. Due to the recent advancement of deep learning, the newly proposed
Deep-RL algorithms have been able to perform extremely well in sophisticated
high-dimensional environments. However, even after successes in many domains,
one of the major challenge in these approaches is the high magnitude of
interactions with the environment required for efficient decision making.
Seeking inspiration from the brain, this problem can be solved by incorporating
instance based learning by biasing the decision making on the memories of high
rewarding experiences. This paper reviews various recent reinforcement learning
methods which incorporate external memory to solve decision making and a survey
of them is presented. We provide an overview of the different methods - along
with their advantages and disadvantages, applications and the standard
experimentation settings used for memory based models. This review hopes to be
a helpful resource to provide key insight of the recent advances in the field
and provide help in further future development of it.
