  Automating the segregation process is a need for every sector experiencing a
high volume of materials handling, repetitive and exhaustive operations, in
addition to risky exposures. Learning automated pick-and-place operations can
be efficiently done by introducing collaborative autonomous systems (e.g.
manipulators) in the workplace and among human operators. In this paper, we
propose a deep reinforcement learning strategy to learn the place task of
multi-categorical items from a shared workspace between dual-manipulators and
to multi-goal destinations, assuming the pick has been already completed. The
learning strategy leverages first a stochastic actor-critic framework to train
an agent's policy network, and second, a dynamic 3D Gym environment where both
static and dynamic obstacles (e.g. human factors and robot mate) constitute the
state space of a Markov decision process. Learning is conducted in a Gazebo
simulator and experiments show an increase in cumulative reward function for
the agent further away from human factors. Future investigations will be
conducted to enhance the task performance for both agents simultaneously.
