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
Exploration and Anti-Exploration with Distributional Random Network Distillation
Kai Yang, Jian Tao, Jiafei Lyu, Xiu Li
Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data
Nathan Lichtlé, Kathy Jang, Adit Shah, Eugene Vinitsky, Jonathan W. Lee, Alexandre M. Bayen
Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents
Quentin Delfosse, Sebastian Sztwiertnia, Mark Rothermel, Wolfgang Stammer, Kristian Kersting
An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control
Antonio Manjavacas, Alejandro Campoy-Nieves, Javier Jiménez-Raboso, Miguel Molina-Solana, Juan Gómez-Romero
Towards Safe Load Balancing based on Control Barrier Functions and Deep Reinforcement Learning
Lam Dinh, Pham Tran Anh Quang, Jérémie Leguay
ReACT: Reinforcement Learning for Controller Parametrization using B-Spline Geometries
Thomas Rudolf, Daniel Flögel, Tobias Schürmann, Simon Süß, Stefan Schwab, Sören Hohmann
Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoring
Samuel Yanes Luis, Dmitriy Shutin, Juan Marchal Gómez, Daniel Gutiérrez Reina, Sergio Toral Marín
i-Rebalance: Personalized Vehicle Repositioning for Supply Demand Balance
Haoyang Chen, Peiyan Sun, Qiyuan Song, Wanyuan Wang, Weiwei Wu, Wencan Zhang, Guanyu Gao, Yan Lyu
Fully Spiking Actor Network with Intra-layer Connections for Reinforcement Learning
Ding Chen, Peixi Peng, Tiejun Huang, Yonghong Tian
Deep Reinforcement Learning for Multi-Truck Vehicle Routing Problems with Multi-Leg Demand Routes
Joshua Levin, Randall Correll, Takanori Ide, Takafumi Suzuki, Takaho Saito, Alan Arai
Learn Once Plan Arbitrarily (LOPA): Attention-Enhanced Deep Reinforcement Learning Method for Global Path Planning
Guoming Huang, Mingxin Hou, Xiaofang Yuan, Shuqiao Huang, Yaonan Wang