Deep Reinforcement Learning
Deep reinforcement learning (DRL) aims to train agents to make optimal decisions in complex environments by learning through trial and error. Current research focuses on improving DRL's robustness, sample efficiency, and interpretability, often employing architectures like Proximal Policy Optimization (PPO), deep Q-networks (DQNs), and graph neural networks (GNNs) to address challenges in diverse applications such as robotics, game playing, and resource management. The resulting advancements have significant implications for various fields, enabling the development of more adaptable and efficient autonomous systems across numerous domains.
Papers
Advancing Forest Fire Prevention: Deep Reinforcement Learning for Effective Firebreak Placement
Lucas Murray, Tatiana Castillo, Jaime Carrasco, Andrés Weintraub, Richard Weber, Isaac Martín de Diego, José Ramón González, Jordi García-Gonzalo
Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing
Cui Zhang, Xiao Xu, Qiong Wu, Pingyi Fan, Qiang Fan, Huiling Zhu, Jiangzhou Wang
TDANet: Target-Directed Attention Network For Object-Goal Visual Navigation With Zero-Shot Ability
Shiwei Lian, Feitian Zhang
RLEMMO: Evolutionary Multimodal Optimization Assisted By Deep Reinforcement Learning
Hongqiao Lian, Zeyuan Ma, Hongshu Guo, Ting Huang, Yue-Jiao Gong
Auto-configuring Exploration-Exploitation Tradeoff in Evolutionary Computation via Deep Reinforcement Learning
Zeyuan Ma, Jiacheng Chen, Hongshu Guo, Yining Ma, Yue-Jiao Gong
Self-organized free-flight arrival for urban air mobility
Martin Waltz, Ostap Okhrin, Michael Schultz
Scaling Population-Based Reinforcement Learning with GPU Accelerated Simulation
Asad Ali Shahid, Yashraj Narang, Vincenzo Petrone, Enrico Ferrentino, Ankur Handa, Dieter Fox, Marco Pavone, Loris Roveda
Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning
Jonathan C. Balloch, Rishav Bhagat, Geigh Zollicoffer, Ruoran Jia, Julia Kim, Mark O. Riedl
Imitation Game: A Model-based and Imitation Learning Deep Reinforcement Learning Hybrid
Eric MSP Veith, Torben Logemann, Aleksandr Berezin, Arlena Wellßow, Stephan Balduin
Game-Theoretic Deep Reinforcement Learning to Minimize Carbon Emissions and Energy Costs for AI Inference Workloads in Geo-Distributed Data Centers
Ninad Hogade, Sudeep Pasricha
MTLight: Efficient Multi-Task Reinforcement Learning for Traffic Signal Control
Liwen Zhu, Peixi Peng, Zongqing Lu, Yonghong Tian