  Pretraining with expert demonstrations have been found useful in speeding up
the training process of deep reinforcement learning algorithms since less
online simulation data is required. Some people use supervised learning to
speed up the process of feature learning, others pretrain the policies by
imitating expert demonstrations. However, these methods are unstable and not
suitable for actor-critic reinforcement learning algorithms. Also, some
existing methods rely on the global optimum assumption, which is not true in
most scenarios. In this paper, we employ expert demonstrations in a
actor-critic reinforcement learning framework, and meanwhile ensure that the
performance is not affected by the fact that expert demonstrations are not
global optimal. We theoretically derive a method for computing policy gradients
and value estimators with only expert demonstrations. Our method is
theoretically plausible for actor-critic reinforcement learning algorithms that
pretrains both policy and value functions. We apply our method to two of the
typical actor-critic reinforcement learning algorithms, DDPG and ACER, and
demonstrate with experiments that our method not only outperforms the RL
algorithms without pretraining process, but also is more simulation efficient.
