  Reinforcement learning is one of the core components in designing an
artificial intelligent system emphasizing real-time response. Reinforcement
learning influences the system to take actions within an arbitrary environment
either having previous knowledge about the environment model or not. In this
paper, we present a comprehensive study on Reinforcement Learning focusing on
various dimensions including challenges, the recent development of different
state-of-the-art techniques, and future directions. The fundamental objective
of this paper is to provide a framework for the presentation of available
methods of reinforcement learning that is informative enough and simple to
follow for the new researchers and academics in this domain considering the
latest concerns. First, we illustrated the core techniques of reinforcement
learning in an easily understandable and comparable way. Finally, we analyzed
and depicted the recent developments in reinforcement learning approaches. My
analysis pointed out that most of the models focused on tuning policy values
rather than tuning other things in a particular state of reasoning.
