Double Critic Deep Reinforcement Learning for Mapless 3D Navigation of Unmanned Aerial Vehicles [article]

Ricardo Bedin Grando, Junior Costa de Jesus, Victor Augusto Kich, Alisson Henrique Kolling, Paulo Lilles Jorge Drews-Jr
2021 arXiv   pre-print
This paper presents a novel deep reinforcement learning-based system for 3D mapless navigation for Unmanned Aerial Vehicles (UAVs). Instead of using a image-based sensing approach, we propose a simple learning system that uses only a few sparse range data from a distance sensor to train a learning agent. We based our approaches on two state-of-art double critic Deep-RL models: Twin Delayed Deep Deterministic Policy Gradient (TD3) and Soft Actor-Critic (SAC). We show that our two approaches
more » ... e to outperform an approach based on the Deep Deterministic Policy Gradient (DDPG) technique and the BUG2 algorithm. Also, our new Deep-RL structure based on Recurrent Neural Networks (RNNs) outperforms the current structure used to perform mapless navigation of mobile robots. Overall, we conclude that Deep-RL approaches based on double critic with Recurrent Neural Networks (RNNs) are better suited to perform mapless navigation and obstacle avoidance of UAVs.
arXiv:2112.13724v1 fatcat:m6cpqs2oefgynjrz7i2p4wemcm