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.

本文提出了一种无地图的移动机器人规避导航的预训练深度强化学习方法，通过将原始传感器数据映射到控制变量并在未知环境中导航。我们提出了高效的离线训练策略，用于加速早期低效的随机探索，并收集了一个包含专家经验的通用数据集，对其他导航训练工作具有一定的意义。全面验证了预训练和优先专家经验可以减少 80％的训练时间，并且已经证实可以提高 DRL 的 2 倍奖励。通过先进的仿真 gazebo 与真实物理建模和动力学方程的结合，减小了仿真与实际之间的差距。我们在走廊环境中训练了我们的模型，并在不同环境中评估了模型，获得了相同的效果。与传统导航方法相比，我们可以确认训练好的模型可以直接应用于不同场景，并具有无碰撞导航的能力。实验证明我们的 DRL 模型具有在不同环境下的通用性能。