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
Safety-Driven Deep Reinforcement Learning Framework for Cobots: A Sim2Real Approach
Ammar N. Abbas, Shakra Mehak, Georgios C. Chasparis, John D. Kelleher, Michael Guilfoyle, Maria Chiara Leva, Aswin K Ramasubramanian
Hierarchical Decoupling Capacitor Optimization for Power Distribution Network of 2.5D ICs with Co-Analysis of Frequency and Time Domains Based on Deep Reinforcement Learning
Yuanyuan Duan, Haiyang Feng, Zhiping Yu, Hanming Wu, Leilai Shao, Xiaolei Zhu
Normalization and effective learning rates in reinforcement learning
Clare Lyle, Zeyu Zheng, Khimya Khetarpal, James Martens, Hado van Hasselt, Razvan Pascanu, Will Dabney
Deep Reinforcement Learning for Adverse Garage Scenario Generation
Kai Li
Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Framework
Xibo Li, Shruti Patel, Christof Büskens
Efficient World Models with Context-Aware Tokenization
Vincent Micheli, Eloi Alonso, François Fleuret
Autonomous Control of a Novel Closed Chain Five Bar Active Suspension via Deep Reinforcement Learning
Nishesh Singh, Sidharth Ramesh, Abhishek Shankar, Jyotishka Duttagupta, Leander Stephen D'Souza, Sanjay Singh
Mixture of Experts in a Mixture of RL settings
Timon Willi, Johan Obando-Ceron, Jakob Foerster, Karolina Dziugaite, Pablo Samuel Castro
Combining Automated Optimisation of Hyperparameters and Reward Shape
Julian Dierkes, Emma Cramer, Holger H. Hoos, Sebastian Trimpe
Breaking the Barrier: Enhanced Utility and Robustness in Smoothed DRL Agents
Chung-En Sun, Sicun Gao, Tsui-Wei Weng
$\text{Alpha}^2$: Discovering Logical Formulaic Alphas using Deep Reinforcement Learning
Feng Xu, Yan Yin, Xinyu Zhang, Tianyuan Liu, Shengyi Jiang, Zongzhang Zhang
Fault Detection for agents on power grid topology optimization: A Comprehensive analysis
Malte Lehna, Mohamed Hassouna, Dmitry Degtyar, Sven Tomforde, Christoph Scholz