Learning to Navigate Through Complex Dynamic Environment With Modular Deep Reinforcement Learning

Abstract

In this paper, we propose an end-to-end modular reinforcement learning architecture for a navigation task in complex dynamic environments with rapidly moving obstacles. In this architecture, the main task is divided into two subtasks: local obstacle avoidance and global navigation. For obstacle avoidance, we develop a two-stream Q-network, which processes spatial and temporal information separately and generates action values. The global navigation subtask is resolved by a conventional Q-network framework. An online learning network and an action scheduler are introduced to first combine two pretrained policies, and then continue exploring and optimizing until a stable policy is obtained. The two-stream Q-network obtains better performance than the conventional deep Q-learning approach in the obstacle avoidance subtask. Experiments on the main task demonstrate that the proposed architecture can efficiently avoid moving obstacles and complete the navigation task at a high success rate. The modular architecture enables parallel training and also demonstrates good generalization capability in different environments.