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
Keep Various Trajectories: Promoting Exploration of Ensemble Policies in Continuous Control
Chao Li, Chen Gong, Qiang He, Xinwen Hou
Sim-to-Real Transfer of Adaptive Control Parameters for AUV Stabilization under Current Disturbance
Thomas Chaffre, Jonathan Wheare, Andrew Lammas, Paulo Santos, Gilles Le Chenadec, Karl Sammut, Benoit Clement
Specialized Deep Residual Policy Safe Reinforcement Learning-Based Controller for Complex and Continuous State-Action Spaces
Ammar N. Abbas, Georgios C. Chasparis, John D. Kelleher
Deep Reinforcement Learning with Explicit Context Representation
Francisco Munguia-Galeano, Ah-Hwee Tan, Ze Ji
Alpha Elimination: Using Deep Reinforcement Learning to Reduce Fill-In during Sparse Matrix Decomposition
Arpan Dasgupta, Pawan Kumar
A Partially Supervised Reinforcement Learning Framework for Visual Active Search
Anindya Sarkar, Nathan Jacobs, Yevgeniy Vorobeychik
Learning RL-Policies for Joint Beamforming Without Exploration: A Batch Constrained Off-Policy Approach
Heasung Kim, Sravan Kumar Ankireddy
Dealing with uncertainty: balancing exploration and exploitation in deep recurrent reinforcement learning
Valentina Zangirolami, Matteo Borrotti
Beyond Traditional DoE: Deep Reinforcement Learning for Optimizing Experiments in Model Identification of Battery Dynamics
Gokhan Budan, Francesca Damiani, Can Kurtulus, N. Kemal Ure
DeePref: Deep Reinforcement Learning For Video Prefetching In Content Delivery Networks
Nawras Alkassab, Chin-Tser Huang, Tania Lorido Botran
Explainable Attention for Few-shot Learning and Beyond
Bahareh Nikpour, Narges Armanfard
Deep Reinforcement Learning for Autonomous Cyber Defence: A Survey
Gregory Palmer, Chris Parry, Daniel J.B. Harrold, Chris Willis
Human-Robot Gym: Benchmarking Reinforcement Learning in Human-Robot Collaboration
Jakob Thumm, Felix Trost, Matthias Althoff
Predictive auxiliary objectives in deep RL mimic learning in the brain
Ching Fang, Kimberly L Stachenfeld
An Open-Loop Baseline for Reinforcement Learning Locomotion Tasks
Antonin Raffin, Olivier Sigaud, Jens Kober, Alin Albu-Schäffer, João Silvério, Freek Stulp
DecAP: Decaying Action Priors for Accelerated Imitation Learning of Torque-Based Legged Locomotion Policies
Shivam Sood, Ge Sun, Peizhuo Li, Guillaume Sartoretti