Hierarchical reinforcement learning: A comprehensive survey

S Pateria, B Subagdja, A Tan, C Quek - ACM Computing Surveys (CSUR …, 2021‏ - dl.acm.org
Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of
challenging long-horizon decision-making tasks into simpler subtasks. During the past …

Auxiliary network enhanced hierarchical graph reinforcement learning for vehicle repositioning

J **, F Zhu, P Ye, Y Lv, G **ong… - IEEE Transactions on …, 2024‏ - ieeexplore.ieee.org
Affected by people's dynamic social activities, the imbalance between vehicle supply and
demand in the Mobility-On-Demand (MOD) system is a common phenomenon. To improve …

Improving Co-existence of URLLC and Distributed AI using RL

W Shi - 2023‏ - diva-portal.org
In 5G, Ultra-reliable and low-latency communications (URLLC) service is envisioned to
enable use cases with strict reliability and latency requirements on wireless communication …

Methods for autonomously decomposing and performing long-horizon sequential decision tasks

S Pateria - 2022‏ - dr.ntu.edu.sg
Sequential decision-making over long timescales and in complex task environments is an
important problem in Artificial Intelligence (AI). An effective approach to tackle this problem is …

Exploiting FX trading patterns at multiple time-scales with hierarchical reinforcement learning

L Zerman - 2021‏ - politesi.polimi.it
Abstract The Foreign Exchange market is the largest financial market in the world and is
therefore an attraction for banks, institutions and individual traders due to its high liquidity …