223 citations · 523 across the 15 of their papers we have counts for
7 papers · 1 filter
Impoola: The Power of Average Pooling for Image-Based Deep Reinforcement Learning
Raphael Trumpp, Ansgar Schäfftlein, Mirco Theile +1
As image-based deep reinforcement learning tackles more challenging tasks, increasing model size has become an important factor in improving performance. Recent studies achieved th…
Action Mapping for Reinforcement Learning in Continuous Environments with Constraints
Mirco Theile, Lukas Dirnberger, Raphael Trumpp +2
Deep reinforcement learning (DRL) has had success across various domains, but applying it to environments with constraints remains challenging due to poor sample efficiency and slo…
Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path Planning
Mirco Theile, Hongpeng Cao, Marco Caccamo +1
In reinforcement learning (RL), exploiting environmental symmetries can significantly enhance efficiency, robustness, and performance. However, ensuring that the deep RL policy and…
Edge Generation Scheduling for DAG Tasks Using Deep Reinforcement Learning
Binqi Sun, Mirco Theile, Ziyuan Qin +4
Directed acyclic graph (DAG) tasks are currently adopted in the real-time domain to model complex applications from the automotive, avionics, and industrial domains that implement…
Learning to Generate All Feasible Actions
Mirco Theile, Daniele Bernardini, Raphael Trumpp +3
Modern cyber-physical systems are becoming increasingly complex to model, thus motivating data-driven techniques such as reinforcement learning (RL) to find appropriate control age…
Cloud-Edge Training Architecture for Sim-to-Real Deep Reinforcement Learning
Hongpeng Cao, Mirco Theile, Federico G. Wyrwal +1
Deep reinforcement learning (DRL) is a promising approach to solve complex control tasks by learning policies through interactions with the environment. However, the training of DR…
UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach
Harald Bayerlein, Mirco Theile, Marco Caccamo +1
Autonomous deployment of unmanned aerial vehicles (UAVs) supporting next-generation communication networks requires efficient trajectory planning methods. We propose a new end-to-e…