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
Benchmarking Deep Reinforcement Learning for Navigation in Denied Sensor Environments
Mariusz Wisniewski, Paraskevas Chatzithanos, Weisi Guo, Antonios Tsourdos
Streaming Deep Reinforcement Learning Finally Works
Mohamed Elsayed, Gautham Vasan, A. Rupam Mahmood
Interpretable end-to-end Neurosymbolic Reinforcement Learning agents
Nils Grandien, Quentin Delfosse, Kristian Kersting
Advanced Persistent Threats (APT) Attribution Using Deep Reinforcement Learning
Animesh Singh Basnet, Mohamed Chahine Ghanem, Dipo Dunsin, Wiktor Sowinski-Mydlarz
Communication-Control Codesign for Large-Scale Wireless Networked Control Systems
Gaoyang Pang, Wanchun Liu, Dusit Niyato, Branka Vucetic, Yonghui Li
Enhancing Robustness in Deep Reinforcement Learning: A Lyapunov Exponent Approach
Rory Young, Nicolas Pugeault
DR-MPC: Deep Residual Model Predictive Control for Real-world Social Navigation
James R. Han, Hugues Thomas, Jian Zhang, Nicholas Rhinehart, Timothy D. Barfoot
Continual Deep Reinforcement Learning to Prevent Catastrophic Forgetting in Jamming Mitigation
Kemal Davaslioglu, Sastry Kompella, Tugba Erpek, Yalin E. Sagduyu
Compositional Shielding and Reinforcement Learning for Multi-Agent Systems
Asger Horn Brorholt, Kim Guldstrand Larsen, Christian Schilling
MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL
Claas A Voelcker, Marcel Hussing, Eric Eaton, Amir-massoud Farahmand, Igor Gilitschenski
FRASA: An End-to-End Reinforcement Learning Agent for Fall Recovery and Stand Up of Humanoid Robots
Clément Gaspard, Marc Duclusaud, Grégoire Passault, Mélodie Daniel, Olivier Ly
Exploring Natural Language-Based Strategies for Efficient Number Learning in Children through Reinforcement Learning
Tirthankar Mittra
Neuroplastic Expansion in Deep Reinforcement Learning
Jiashun Liu, Johan Obando-Ceron, Aaron Courville, Ling Pan
Masked Generative Priors Improve World Models Sequence Modelling Capabilities
Cristian Meo, Mircea Lica, Zarif Ikram, Akihiro Nakano, Vedant Shah, Aniket Rajiv Didolkar, Dianbo Liu, Anirudh Goyal, Justin Dauwels
The Power of Input: Benchmarking Zero-Shot Sim-To-Real Transfer of Reinforcement Learning Control Policies for Quadrotor Control
Alberto Dionigi, Gabriele Costante, Giuseppe Loianno