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
Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach
Iman Sharifi, Mustafa Yildirim, Saber Fallah
Enhancing the Robustness of QMIX against State-adversarial Attacks
Weiran Guo, Guanjun Liu, Ziyuan Zhou, Ling Wang, Jiacun Wang
GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling over Dynamic Vehicular Clouds
Zhang Liu, Lianfen Huang, Zhibin Gao, Manman Luo, Seyyedali Hosseinalipour, Huaiyu Dai
Identifying Important Sensory Feedback for Learning Locomotion Skills
Wanming Yu, Chuanyu Yang, Christopher McGreavy, Eleftherios Triantafyllidis, Guillaume Bellegarda, Milad Shafiee, Auke Jan Ijspeert, Zhibin Li
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
Federico Berto, Chuanbo Hua, Junyoung Park, Laurin Luttmann, Yining Ma, Fanchen Bu, Jiarui Wang, Haoran Ye, Minsu Kim, Sanghyeok Choi, Nayeli Gast Zepeda, André Hottung, Jianan Zhou, Jieyi Bi, Yu Hu, Fei Liu, Hyeonah Kim, Jiwoo Son, Haeyeon Kim, Davide Angioni, Wouter Kool, Zhiguang Cao, Qingfu Zhang, Joungho Kim, Jie Zhang, Kijung Shin, Cathy Wu, Sungsoo Ahn, Guojie Song, Changhyun Kwon, Kevin Tierney, Lin Xie, Jinkyoo Park
Learning Coverage Paths in Unknown Environments with Deep Reinforcement Learning
Arvi Jonnarth, Jie Zhao, Michael Felsberg
Learning Environment Models with Continuous Stochastic Dynamics
Martin Tappler, Edi Muškardin, Bernhard K. Aichernig, Bettina Könighofer
Eigensubspace of Temporal-Difference Dynamics and How It Improves Value Approximation in Reinforcement Learning
Qiang He, Tianyi Zhou, Meng Fang, Setareh Maghsudi