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
A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning
Arthur Juliani, Jordan T. Ash
Proactive Load-Shaping Strategies with Privacy-Cost Trade-offs in Residential Households based on Deep Reinforcement Learning
Ruichang Zhang, Youcheng Sun, Mustafa A. Mustafa
Advancing Household Robotics: Deep Interactive Reinforcement Learning for Efficient Training and Enhanced Performance
Arpita Soni, Sujatha Alla, Suresh Dodda, Hemanth Volikatla
Ego-Foresight: Agent Visuomotor Prediction as Regularization for RL
Manuel S. Nunes, Atabak Dehban, Yiannis Demiris, José Santos-Victor
Biological Neurons Compete with Deep Reinforcement Learning in Sample Efficiency in a Simulated Gameworld
Moein Khajehnejad, Forough Habibollahi, Aswin Paul, Adeel Razi, Brett J. Kagan
SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning
Shuai Zhang, Heshan Devaka Fernando, Miao Liu, Keerthiram Murugesan, Songtao Lu, Pin-Yu Chen, Tianyi Chen, Meng Wang
Spatio-temporal Value Semantics-based Abstraction for Dense Deep Reinforcement Learning
Jihui Nie, Dehui Du, Jiangnan Zhao
Multi-turn Reinforcement Learning from Preference Human Feedback
Lior Shani, Aviv Rosenberg, Asaf Cassel, Oran Lang, Daniele Calandriello, Avital Zipori, Hila Noga, Orgad Keller, Bilal Piot, Idan Szpektor, Avinatan Hassidim, Yossi Matias, Rémi Munos
Deep Reinforcement Learning for 5*5 Multiplayer Go
Brahim Driss, Jérôme Arjonilla, Hui Wang, Abdallah Saffidine, Tristan Cazenave
A Behavior-Aware Approach for Deep Reinforcement Learning in Non-stationary Environments without Known Change Points
Zihe Liu, Jie Lu, Guangquan Zhang, Junyu Xuan
Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates
Udayan Mandal, Guy Amir, Haoze Wu, Ieva Daukantas, Fletcher Lee Newell, Umberto J. Ravaioli, Baoluo Meng, Michael Durling, Milan Ganai, Tobey Shim, Guy Katz, Clark Barrett
Uncertainty-Aware DRL for Autonomous Vehicle Crowd Navigation in Shared Space
Mahsa Golchoubian, Moojan Ghafurian, Kerstin Dautenhahn, Nasser Lashgarian Azad