  Robots often face situations where grasping a goal object is desirable but
not feasible due to other present objects preventing the grasp action. We
present a deep Reinforcement Learning approach to learn grasping and pushing
policies for manipulating a goal object in highly cluttered environments to
address this problem. In particular, a dual Reinforcement Learning model
approach is proposed, which presents high resilience in handling complicated
scenes, reaching an average of 98% task completion using primitive objects in a
simulation environment. To evaluate the performance of the proposed approach,
we performed two extensive sets of experiments in packed objects and a pile of
object scenarios with a total of 1000 test runs in simulation. Experimental
results showed that the proposed method worked very well in both scenarios and
outperformed the recent state-of-the-art approaches. Demo video, trained
models, and source code for the results reproducibility purpose are publicly
available. https://sites.google.com/view/pushandgrasp/home
