Resilient Navigation Among Dynamic Agents with Hierarchical Reinforcement Learning

Behaving safe and efficient navigation policy without knowing surrounding agents’ intent is a hard problem. This problem is challenging for two reasons: the agent need to face high environment uncertainty for it can’t control other agents in the environment. Moreover, the navigation algorithm need to be resilient to various scenes. Recently reinforcement learning based navigation has attracted researchers interest. We present a hierarchical reinforcement learning based navigation algorithm. The two-level structure decouples the navigation task into target driven and collision avoidance, leading to a faster and more stable model to be trained. Compared with the reinforcement learning based navigation methods in recent years, we verified our model on navigation ability and the resilience on different scenes.