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
Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimization
Hyeonah Kim, Minsu Kim, Sungsoo Ahn, Jinkyoo Park
Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance
Weizhi Nie, Chen Zhang, Dan Song, Lina Zhao, Yunpeng Bai, Keliang Xie, Anan Liu
Multi-environment lifelong deep reinforcement learning for medical imaging
Guangyao Zheng, Shuhao Lai, Vladimir Braverman, Michael A. Jacobs, Vishwa S. Parekh
Simulation and Retargeting of Complex Multi-Character Interactions
Yunbo Zhang, Deepak Gopinath, Yuting Ye, Jessica Hodgins, Greg Turk, Jungdam Won
Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning
Kyowoon Lee, Seongun Kim, Jaesik Choi
Perimeter Control Using Deep Reinforcement Learning: A Model-free Approach towards Homogeneous Flow Rate Optimization
Xiaocan Li, Ray Coden Mercurius, Ayal Taitler, Xiaoyu Wang, Mohammad Noaeen, Scott Sanner, Baher Abdulhai
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
Haque Ishfaq, Qingfeng Lan, Pan Xu, A. Rupam Mahmood, Doina Precup, Anima Anandkumar, Kamyar Azizzadenesheli
Action valuation of on- and off-ball soccer players based on multi-agent deep reinforcement learning
Hiroshi Nakahara, Kazushi Tsutsui, Kazuya Takeda, Keisuke Fujii
Counterfactual Explainer Framework for Deep Reinforcement Learning Models Using Policy Distillation
Amir Samadi, Konstantinos Koufos, Kurt Debattista, Mehrdad Dianati
Market Making with Deep Reinforcement Learning from Limit Order Books
Hong Guo, Jianwu Lin, Fanlin Huang
Lucy-SKG: Learning to Play Rocket League Efficiently Using Deep Reinforcement Learning
Vasileios Moschopoulos, Pantelis Kyriakidis, Aristotelis Lazaridis, Ioannis Vlahavas
Solving Stabilize-Avoid Optimal Control via Epigraph Form and Deep Reinforcement Learning
Oswin So, Chuchu Fan
RLBoost: Boosting Supervised Models using Deep Reinforcement Learning
Eloy Anguiano Batanero, Ángela Fernández Pascual, Álvaro Barbero Jiménez
Research on Multi-Agent Communication and Collaborative Decision-Making Based on Deep Reinforcement Learning
Zeng Da
Control of a simulated MRI scanner with deep reinforcement learning
Simon Walker-Samuel
Semantic-aware Transmission Scheduling: a Monotonicity-driven Deep Reinforcement Learning Approach
Jiazheng Chen, Wanchun Liu, Daniel Quevedo, Yonghui Li, Branka Vucetic