  In recent years deep neural networks have been successfully applied to the
domains of reinforcement learning
\cite{bengio2009learning,krizhevsky2012imagenet,hinton2006reducing}. Deep
reinforcement learning \cite{mnih2015human} is reported to have the advantage
of learning effective policies directly from high-dimensional sensory inputs
over traditional agents. However, within the scope of the literature, there is
no fundamental change or improvement on the existing training framework. Here
we propose a novel training framework that is conceptually comprehensible and
potentially easy to be generalized to all feasible algorithms for reinforcement
learning. We employ Monte-carlo sampling to achieve raw data inputs, and train
them in batch to achieve Markov decision process sequences and synchronously
update the network parameters instead of experience replay. This training
framework proves to optimize the unbiased approximation of loss function whose
estimation exactly matches the real probability distribution data inputs
follow, and thus have overwhelming advantages of sample efficiency and
convergence rate over existing deep reinforcement learning after evaluating it
on both discrete action spaces and continuous control problems. Besides, we
propose several algorithms embedded with our new framework to deal with typical
discrete and continuous scenarios. These algorithms prove to be far more
efficient than their original versions under the framework of deep
reinforcement learning, and provide examples for existing and future algorithms
to generalize to our new framework.
