  One of the biggest hurdles robotics faces is the facet of sophisticated and
hard-to-engineer behaviors. Reinforcement learning offers a set of tools, and a
framework to address this problem. In parallel, the misgivings of robotics
offer a solid testing ground and evaluation metric for advancements in
reinforcement learning. The two disciplines go hand-in-hand, much like the
fields of Mathematics and Physics. By means of this survey paper, we aim to
invigorate links between the research communities of the two disciplines by
focusing on the work done in reinforcement learning for locomotive and control
aspects of robotics. Additionally, we aim to highlight not only the notable
successes but also the key challenges of the application of Reinforcement
Learning in Robotics. This paper aims to serve as a reference guide for
researchers in reinforcement learning applied to the field of robotics. The
literature survey is at a fairly introductory level, aimed at aspiring
researchers. Appropriately, we have covered the most essential concepts
required for research in the field of reinforcement learning, with robotics in
mind. Through a thorough analysis of this problem, we are able to manifest how
reinforcement learning could be applied profitably, and also focus on
open-ended questions, as well as the potential for future research.
