  Reinforcement learning (RL) algorithms find applications in inventory
control, recommender systems, vehicular traffic management, cloud computing and
robotics. The real-world complications of many tasks arising in these domains
makes them difficult to solve with the basic assumptions underlying classical
RL algorithms. RL agents in these applications often need to react and adapt to
changing operating conditions. A significant part of research on single-agent
RL techniques focuses on developing algorithms when the underlying assumption
of stationary environment model is relaxed. This paper provides a survey of RL
methods developed for handling dynamically varying environment models. The goal
of methods not limited by the stationarity assumption is to help autonomous
agents adapt to varying operating conditions. This is possible either by
minimizing the rewards lost during learning by RL agent or by finding a
suitable policy for the RL agent which leads to efficient operation of the
underlying system. A representative collection of these algorithms is discussed
in detail in this work along with their categorization and their relative
merits and demerits. Additionally we also review works which are tailored to
application domains. Finally, we discuss future enhancements for this field.
