MEMORY-AUGMENTED REINFORCEMENT LEARNING FOR UAV NAVIGATION USING PPO-LSTM

Abstract

The problems of partial observability and sensor shortage pose a significant challenge for autonomous Unmanned Aerial Vehicles (UAVs) as they prove to be challenging for conventional Deep Reinforcement Learning (DRL) methods to undertake well under such conditions. In this paper, a memory-augmented Proximal Policy Optimization (PPO) model extended using a Long Short-Term Memory (LSTM) network is proposed as a solution to such challenges. The observation space is constructed from 2D LiDAR and Inertial Measurement Unit (IMU) data to sense simultaneously external observation and internal state of motion, whereas the action space consists of continuous velocity commands. A shaped reward function is optimized for encouraging safe target approaching, obstacle avoidance, and convergence speed. Experimental outcomes show that the PPO-LSTM described herein achieves smoother paths, more robust reward convergence, and a much lower rate of collision than regular PPO. It also generalizes to new environments with movable obstacles. Qualitatively, the success rate increased from 64.5% to 83.9%, collision frequency reduced by over 70%, and path efficiency increased from 0.60 to 0.85, without suffering from unstable training behavior