Avoidance Navigation Based on Offline Pre-Training Reinforcement Learning
This work addresses the challenge of training time and generalization for mobile robot navigation in unknown environments, though it appears incremental as it builds on existing DRL approaches with specific optimizations.
The paper tackles the problem of inefficient early-stage exploration in deep reinforcement learning for robot navigation by proposing an offline pre-training strategy with prioritized expert experience, which reduces training time by 80% and doubles the reward compared to traditional methods.
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.