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
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL
Jesse Farebrother, Jordi Orbay, Quan Vuong, Adrien Ali Taïga, Yevgen Chebotar, Ted Xiao, Alex Irpan, Sergey Levine, Pablo Samuel Castro, Aleksandra Faust, Aviral Kumar, Rishabh Agarwal
Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning
Zida Wu, Mathieu Lauriere, Samuel Jia Cong Chua, Matthieu Geist, Olivier Pietquin, Ankur Mehta
Twisting Lids Off with Two Hands
Toru Lin, Zhao-Heng Yin, Haozhi Qi, Pieter Abbeel, Jitendra Malik
Deep Reinforcement Learning for Dynamic Algorithm Selection: A Proof-of-Principle Study on Differential Evolution
Hongshu Guo, Yining Ma, Zeyuan Ma, Jiacheng Chen, Xinglin Zhang, Zhiguang Cao, Jun Zhang, Yue-Jiao Gong
Iterated $Q$-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning
Théo Vincent, Daniel Palenicek, Boris Belousov, Jan Peters, Carlo D'Eramo
An Efficient Model-Based Approach on Learning Agile Motor Skills without Reinforcement
Haojie Shi, Tingguang Li, Qingxu Zhu, Jiapeng Sheng, Lei Han, Max Q. -H. Meng
Learning to Solve Job Shop Scheduling under Uncertainty
Guillaume Infantes, Stéphanie Roussel, Pierre Pereira, Antoine Jacquet, Emmanuel Benazera
Snapshot Reinforcement Learning: Leveraging Prior Trajectories for Efficiency
Yanxiao Zhao, Yangge Qian, Tianyi Wang, Jingyang Shan, Xiaolin Qin
Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey
Lucas Schott, Josephine Delas, Hatem Hajri, Elies Gherbi, Reda Yaich, Nora Boulahia-Cuppens, Frederic Cuppens, Sylvain Lamprier
Learning Quadrupedal Locomotion with Impaired Joints Using Random Joint Masking
Mincheol Kim, Ukcheol Shin, Jung-Yup Kim
Robustifying a Policy in Multi-Agent RL with Diverse Cooperative Behaviors and Adversarial Style Sampling for Assistive Tasks
Takayuki Osa, Tatsuya Harada
Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode
Jinyang Jiang, Xiaotian Liu, Tao Ren, Qinghao Wang, Yi Zheng, Yufu Du, Yijie Peng, Cheng Zhang
Cloud-based Federated Learning Framework for MRI Segmentation
Rukesh Prajapati, Amr S. El-Wakeel
Deep Reinforcement Learning: A Convex Optimization Approach
Ather Gattami
Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets
Jinhao Li, Changlong Wang, Yanru Zhang, Hao Wang
A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations
Erhan Can Ozcan, Vittorio Giammarino, James Queeney, Ioannis Ch. Paschalidis