Danger-aware Adaptive Composition of DRL Agents for Self-navigation

Understand

Self-navigation, referred as the capability of automatically reaching the goal while avoiding collisions with obstacles, is a fundamental skill required for mobile robots.

• Recently, deep reinforcement learning (DRL) has shown great potential in the development of robot navigation algorithms.
• However, it is still difficult to train the robot to learn goal-reaching and obstacle-avoidance skills simultaneously.
• On the other hand, although many DRL-based obstacle-avoidance algorithms are proposed, few of them are reused for more complex navigation tasks.