  Deep Reinforcement Learning (DRL) aims to create intelligent agents that can
learn to solve complex problems efficiently in a real-world environment.
Typically, two learning goals: adaptation and generalization are used for
baselining DRL algorithm's performance on different tasks and domains. This
paper presents a survey on the recent developments in DRL-based approaches for
adaptation and generalization. We begin by formulating these goals in the
context of task and domain. Then we review the recent works under those
approaches and discuss future research directions through which DRL algorithms'
adaptability and generalizability can be enhanced and potentially make them
applicable to a broad range of real-world problems.
