  This paper presents a Pre-Training Deep Reinforcement Learning(DRL) for
avoidance navigation without map for mobile robots which map raw sensor data to
control variable and navigate in an unknown environment. The efficient offline
training strategy is proposed to speed up the inefficient random explorations
in early stage and we also collect a universal dataset including expert
experience for offline training, which is of some significance for other
navigation training work. The pre-training and prioritized expert experience
are proposed to reduce 80\% training time and has been verified to improve the
2 times reward of DRL. The advanced simulation gazebo with real physical
modelling and dynamic equations reduce the gap between sim-to-real. We train
our model a corridor environment, and evaluate the model in different
environment getting the same effect. Compared to traditional method navigation,
we can confirm the trained model can be directly applied into different
scenarios and have the ability to no collision navigate. It was demonstrated
that our DRL model have universal general capacity in different environment.
