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
Neural Policy Style Transfer
Raul Fernandez-Fernandez, Juan G. Victores, Jennifer J. Gago, David Estevez, Carlos Balaguer
Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk Management
Zhenglong Li, Vincent Tam, Kwan L. Yeung
RadDQN: a Deep Q Learning-based Architecture for Finding Time-efficient Minimum Radiation Exposure Pathway
Biswajit Sadhu, Trijit Sadhu, S. Anand
AlphaRank: An Artificial Intelligence Approach for Ranking and Selection Problems
Ruihan Zhou, L. Jeff Hong, Yijie Peng
Circuit Partitioning for Multi-Core Quantum Architectures with Deep Reinforcement Learning
Arnau Pastor, Pau Escofet, Sahar Ben Rached, Eduard Alarcón, Pere Barlet-Ros, Sergi Abadal
Attention Graph for Multi-Robot Social Navigation with Deep Reinforcement Learning
Erwan Escudie, Laetitia Matignon, Jacques Saraydaryan
Curriculum-Based Reinforcement Learning for Quadrupedal Jumping: A Reference-free Design
Vassil Atanassov, Jiatao Ding, Jens Kober, Ioannis Havoutis, Cosimo Della Santina
Effective Communication with Dynamic Feature Compression
Pietro Talli, Francesco Pase, Federico Chiariotti, Andrea Zanella, Michele Zorzi
A Deep Q-Network Based on Radial Basis Functions for Multi-Echelon Inventory Management
Liqiang Cheng, Jun Luo, Weiwei Fan, Yidong Zhang, Yuan Li
Attentive Convolutional Deep Reinforcement Learning for Optimizing Solar-Storage Systems in Real-Time Electricity Markets
Jinhao Li, Changlong Wang, Hao Wang