  The focus of this work is to enumerate the various approaches and algorithms
that center around application of reinforcement learning in robotic ma-
]]nipulation tasks. Earlier methods utilized specialized policy representations
and human demonstrations to constrict the policy. Such methods worked well with
continuous state and policy space of robots but failed to come up with
generalized policies. Subsequently, high dimensional non-linear function
approximators like neural networks have been used to learn policies from
scratch. Several novel and recent approaches have also embedded control policy
with efficient perceptual representation using deep learning. This has led to
the emergence of a new branch of dynamic robot control system called deep r
inforcement learning(DRL). This work embodies a survey of the most recent
algorithms, architectures and their implementations in simulations and real
world robotic platforms. The gamut of DRL architectures are partitioned into
two different branches namely, discrete action space algorithms(DAS) and
continuous action space algorithms(CAS). Further, the CAS algorithms are
divided into stochastic continuous action space(SCAS) and deterministic
continuous action space(DCAS) algorithms. Along with elucidating an organ-
isation of the DRL algorithms this work also manifests some of the state of the
art applications of these approaches in robotic manipulation tasks.
