UAV Path Planning with Reinforcement Learning Mirco Theile, Harald Bayerlein Mar 1, 2020 Reinforcement Learning UAV Path Planning Mirco Theile Postdoctoral Researcher in Reinforcement Learning Harald Bayerlein Postdoc Publications Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path Planning This paper describes a method to exploit environmental symmetries in reinforcement learning by constructing equivariant policies and invariant value functions without specialized neural network components. We show how equivariant ensembles and regularization benefit sample efficiency and performance in a map-based path planning case study. Mirco Theile, Hongpeng Cao, Marco Caccamo, Alberto L. Sangiovanni-Vincentelli PDF Cite Code Project Video DOI Learning to Recharge: UAV Coverage Path Planning through Deep Reinforcement Learning To solve the power-constrained UAV coverage path planning problem with recharge, we propose a novel proximal policy optimization (PPO)-based deep reinforcement learning (DRL) approach with map-based observations, utilizing action masking and discount factor scheduling to optimize coverage trajectories over the entire mission horizon. Mirco Theile, Harald Bayerlein, Marco Caccamo, Alberto L. Sangiovanni-Vincentelli PDF Cite Code Project Video DOI UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning We show that UAV path planning can be made scalable by presenting the agent with a coarse but complete global map and a precise but incomplete local map of the environment. Mirco Theile, Harald Bayerlein, Richard Nai, David Gesbert, Marco Caccamo PDF Cite Code Project DOI Multi-UAV Path Planning for Wireless Data Harvesting With Deep Reinforcement Learning We propose to train a DDQN agent based on global and local map data to learn collaborative decentralized multi-agent policies for UAV data harvesting missions. Harald Bayerlein, Mirco Theile, Marco Caccamo, David Gesbert PDF Cite Code Project DOI UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach Autonomous deployment of unmanned aerial vehicles (UAVs) supporting next-generation communication networks requires efficient … Harald Bayerlein, Mirco Theile, Marco Caccamo, David Gesbert PDF Cite Code Project DOI UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning Coverage path planning (CPP) is the task of designing a trajectory that enables a mobile agent to travel over every point of an area of … Mirco Theile, Harald Bayerlein, Richard Nai, David Gesbert, Marco Caccamo PDF Cite Code Project DOI
