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
Adaptive trajectory-constrained exploration strategy for deep reinforcement learning
Guojian Wang, Faguo Wu, Xiao Zhang, Ning Guo, Zhiming Zheng
Visual Spatial Attention and Proprioceptive Data-Driven Reinforcement Learning for Robust Peg-in-Hole Task Under Variable Conditions
André Yuji Yasutomi, Hideyuki Ichiwara, Hiroshi Ito, Hiroki Mori, Tetsuya Ogata
A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration
Fahri Wisnu Murti, Samad Ali, Matti Latva-aho
PDiT: Interleaving Perception and Decision-making Transformers for Deep Reinforcement Learning
Hangyu Mao, Rui Zhao, Ziyue Li, Zhiwei Xu, Hao Chen, Yiqun Chen, Bin Zhang, Zhen Xiao, Junge Zhang, Jiangjin Yin
Swap-based Deep Reinforcement Learning for Facility Location Problems in Networks
Wenxuan Guo, Yanyan Xu, Yaohui Jin
A Target Detection Algorithm in Traffic Scenes Based on Deep Reinforcement Learning
Xinyu Ren, Ruixuan Wang
VAE for Modified 1-Hot Generative Materials Modeling, A Step Towards Inverse Material Design
Khalid El-Awady