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
Learning Off-policy with Model-based Intrinsic Motivation For Active Online Exploration
Yibo Wang, Jiang Zhao
RL-MUL 2.0: Multiplier Design Optimization with Parallel Deep Reinforcement Learning and Space Reduction
Dongsheng Zuo, Jiadong Zhu, Yikang Ouyang, Yuzhe Ma
Variational Autoencoders for exteroceptive perception in reinforcement learning-based collision avoidance
Thomas Nakken Larsen, Eirik Runde Barlaug, Adil Rasheed
Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey
Yiyang Chen, Chao Ji, Yunrui Cai, Tong Yan, Bo Su
Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Perspectives
Runze Lin, Junghui Chen, Lei Xie, Hongye Su, Biao Huang
AirPilot: Interpretable PPO-based DRL Auto-Tuned Nonlinear PID Drone Controller for Robust Autonomous Flights
Junyang Zhang, Cristian Emanuel Ocampo Rivera, Kyle Tyni, Steven Nguyen, Ulices Santa Cruz Leal, Yasser Shoukry
Path planning of magnetic microswimmers in high-fidelity simulations of capillaries with deep reinforcement learning
Lucas Amoudruz, Sergey Litvinov, Petros Koumoutsakos
Biologically-Plausible Topology Improved Spiking Actor Network for Efficient Deep Reinforcement Learning
Duzhen Zhang, Qingyu Wang, Tielin Zhang, Bo Xu
Fusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overview
Yingbai Hu, Fares J. Abu-Dakka, Fei Chen, Xiao Luo, Zheng Li, Alois Knoll, Weiping Ding
CaT: Constraints as Terminations for Legged Locomotion Reinforcement Learning
Elliot Chane-Sane, Pierre-Alexandre Leziart, Thomas Flayols, Olivier Stasse, Philippe Souères, Nicolas Mansard
Intent-Aware DRL-Based Uplink Dynamic Scheduler for 5G-NR
Salwa Mostafa, Mateus P. Mota, Alvaro Valcarce, Mehdi Bennis
SRLM: Human-in-Loop Interactive Social Robot Navigation with Large Language Model and Deep Reinforcement Learning
Weizheng Wang, Ike Obi, Byung-Cheol Min
Parametric PDE Control with Deep Reinforcement Learning and Differentiable L0-Sparse Polynomial Policies
Nicolò Botteghi, Urban Fasel
Improved Long Short-Term Memory-based Wastewater Treatment Simulators for Deep Reinforcement Learning
Esmaeel Mohammadi, Daniel Ortiz-Arroyo, Mikkel Stokholm-Bjerregaard, Aviaja Anna Hansen, Petar Durdevic