Deep Reinforcement Learning with New-Field Exploration for Navigation in Detour Environment

Abstract

Deep Reinforcement Learning (DRL) has made a great progress in recent years with the development of many relative researching areas, such as Deep Learning. Researchers have trained agents to achieve human-level and even beyond human-level scores in video games by using DRL. In the field of robotics, DRL can also achieve satisfactory performance for the navigation task when the environment is relatively simple. However, when environments become complex, e.g., the detour ones, the DRL system often fails to attain good results. To tackle this problem, we propose an internal reward obtaining method called New-Field-Explore (NFE) mechanism which can navigate a robot from initial position to target position without collision in detour environments. We also present a benchmark suite based on the AI2-Thor environment for robot navigation in complex detour environments. The proposed method is evaluated in these environments by comparing the performance of state-of-the-art algorithms with or without the NFE mechanism1. Experimental results show the above reward is effective for mobile robot navigation tasks in detour indoor environments.