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
Optimizing Automatic Differentiation with Deep Reinforcement Learning
Jamie Lohoff, Emre Neftci
Probabilistic Perspectives on Error Minimization in Adversarial Reinforcement Learning
Roman Belaire, Arunesh Sinha, Pradeep Varakantham
Optimization of geological carbon storage operations with multimodal latent dynamic model and deep reinforcement learning
Zhongzheng Wang, Yuntian Chen, Guodong Chen, Dongxiao Zhang
GenSafe: A Generalizable Safety Enhancer for Safe Reinforcement Learning Algorithms Based on Reduced Order Markov Decision Process Model
Zhehua Zhou, Xuan Xie, Jiayang Song, Zhan Shu, Lei Ma
Exploring Pessimism and Optimism Dynamics in Deep Reinforcement Learning
Bahareh Tasdighi, Nicklas Werge, Yi-Shan Wu, Melih Kandemir
A Generalized Apprenticeship Learning Framework for Modeling Heterogeneous Student Pedagogical Strategies
Md Mirajul Islam, Xi Yang, John Hostetter, Adittya Soukarjya Saha, Min Chi
By Fair Means or Foul: Quantifying Collusion in a Market Simulation with Deep Reinforcement Learning
Michael Schlechtinger, Damaris Kosack, Franz Krause, Heiko Paulheim
Improving Generalization in Aerial and Terrestrial Mobile Robots Control Through Delayed Policy Learning
Ricardo B. Grando, Raul Steinmetz, Victor A. Kich, Alisson H. Kolling, Pablo M. Furik, Junior C. de Jesus, Bruna V. Guterres, Daniel T. Gamarra, Rodrigo S. Guerra, Paulo L. J. Drews-Jr
Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy
Riqiang Gao, Florin C. Ghesu, Simon Arberet, Shahab Basiri, Esa Kuusela, Martin Kraus, Dorin Comaniciu, Ali Kamen
Learning the Target Network in Function Space
Kavosh Asadi, Yao Liu, Shoham Sabach, Ming Yin, Rasool Fakoor
Deep Reinforcement Learning Behavioral Mode Switching Using Optimal Control Based on a Latent Space Objective
Sindre Benjamin Remman, Bjørn Andreas Kristiansen, Anastasios M. Lekkas
Advancing DRL Agents in Commercial Fighting Games: Training, Integration, and Agent-Human Alignment
Chen Zhang, Qiang He, Zhou Yuan, Elvis S. Liu, Hong Wang, Jian Zhao, Yang Wang
Deep reinforcement learning for weakly coupled MDP's with continuous actions
Francisco Robledo, Urtzi Ayesta, Konstantin Avrachenkov
Research on the Application of Computer Vision Based on Deep Learning in Autonomous Driving Technology
Jingyu Zhang, Jin Cao, Jinghao Chang, Xinjin Li, Houze Liu, Zhenglin Li
Towards Learning Foundation Models for Heuristic Functions to Solve Pathfinding Problems
Vedant Khandelwal, Amit Sheth, Forest Agostinelli