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
Physics-Aware Combinatorial Assembly Planning using Deep Reinforcement Learning
Ruixuan Liu, Alan Chen, Weiye Zhao, Changliu Liu
Efficient Exploration in Deep Reinforcement Learning: A Novel Bayesian Actor-Critic Algorithm
Nikolai Rozanov
Demystifying Reinforcement Learning in Production Scheduling via Explainable AI
Daniel Fischer, Hannah M. Hüsener, Felix Grumbach, Lukas Vollenkemper, Arthur Müller, Pascal Reusch
An Efficient Deep Reinforcement Learning Model for Online 3D Bin Packing Combining Object Rearrangement and Stable Placement
Peiwen Zhou, Ziyan Gao, Chenghao Li, Nak Young Chong
Reinforcement Learning Compensated Model Predictive Control for Off-road Driving on Unknown Deformable Terrain
Prakhar Gupta, Jonathon M. Smereka, Yunyi Jia
DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoV
Xueying Gu, Qiong Wu, Pingyi Fan, Qiang Fan, Nan Cheng, Wen Chen, Khaled B. Letaief
Parallel Distributional Deep Reinforcement Learning for Mapless Navigation of Terrestrial Mobile Robots
Victor Augusto Kich, Alisson Henrique Kolling, Junior Costa de Jesus, Gabriel V. Heisler, Hiago Jacobs, Jair Augusto Bottega, André L. da S. Kelbouscas, Akihisa Ohya, Ricardo Bedin Grando, Paulo Lilles Jorge Drews-Jr, Daniel Fernando Tello Gamarra
DeepAir: A Multi-Agent Deep Reinforcement Learning Based Scheme for an Unknown User Location Problem
Baris Yamansavascilar, Atay Ozgovde, Cem Ersoy
Model-Based Transfer Learning for Contextual Reinforcement Learning
Jung-Hoon Cho, Vindula Jayawardana, Sirui Li, Cathy Wu
Deep Reinforcement Learning for the Design of Metamaterial Mechanisms with Functional Compliance Control
Yejun Choi, Yeoneung Kim, Keun Park
KnowPC: Knowledge-Driven Programmatic Reinforcement Learning for Zero-shot Coordination
Yin Gu, Qi Liu, Zhi Li, Kai Zhang
HDPlanner: Advancing Autonomous Deployments in Unknown Environments through Hierarchical Decision Networks
Jingsong Liang, Yuhong Cao, Yixiao Ma, Hanqi Zhao, Guillaume Sartoretti
RL-ADN: A High-Performance Deep Reinforcement Learning Environment for Optimal Energy Storage Systems Dispatch in Active Distribution Networks
Shengren Hou, Shuyi Gao, Weijie Xia, Edgar Mauricio Salazar Duque, Peter Palensky, Pedro P. Vergara
Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes
Chen Tang, Ben Abbatematteo, Jiaheng Hu, Rohan Chandra, Roberto Martín-Martín, Peter Stone
Context-aware Mamba-based Reinforcement Learning for social robot navigation
Syed Muhammad Mustafa, Omema Rizvi, Zain Ahmed Usmani, Abdul Basit Memon, Muhammad Mobeen Movania
Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning
Seyeon Kim, Joonhun Lee, Namhoon Cho, Sungjun Han, Wooseop Hwang