Meta-reinforcement learning by tracking task non-stationarity
Many real-world domains are subject to a structured non-stationarity which affects the
agent's goals and the environmental dynamics. Meta-reinforcement learning (RL) has been …
agent's goals and the environmental dynamics. Meta-reinforcement learning (RL) has been …
[HTML][HTML] Quantifying the impact of non-stationarity in reinforcement learning-based traffic signal control
In reinforcement learning (RL), dealing with non-stationarity is a challenging issue.
However, some domains such as traffic optimization are inherently non-stationary. Causes …
However, some domains such as traffic optimization are inherently non-stationary. Causes …
Integrating reinforcement learning with deterministic learning for fault diagnosis of nonlinear systems
Z Zhu, W Wu, T Chen, J Hu, C Wang - Neurocomputing, 2023 - Elsevier
Reliable fault diagnosis (FD) is important to ensure safety in nonlinear engineering systems.
Modern engineering systems are often subject to unknown complex nonlinearities and …
Modern engineering systems are often subject to unknown complex nonlinearities and …
Wineinformatics: can wine reviews in bordeaux reveal wine aging capability?
Wineinformatics is a new and emerging data science that uses wine as domain knowledge
and integrates data systems and wine-related data sets. Wine reviews from Wine Spectator …
and integrates data systems and wine-related data sets. Wine reviews from Wine Spectator …
Hierarchical Reinforcement Learning-Based Routing Algorithm With Grouped RSU in Urban VANETs
The rapid growth of the Internet of Vehicles (IoV) has generated significant interest in routing
techniques for vehicular ad hoc networks (VANETs) in both academic and industrial …
techniques for vehicular ad hoc networks (VANETs) in both academic and industrial …
Application of Reinforcement Learning to UR10 Positioning for Prioritized Multi-Step Inspection in NVIDIA Omniverse
F Bahrpeyma, A Sunilkumar… - 2023 IEEE Symposium …, 2023 - ieeexplore.ieee.org
Sequential multi-step operations have long played an important role in manufacturing
systems. In high level multi-step manufacturing processes, multiple operations are carried …
systems. In high level multi-step manufacturing processes, multiple operations are carried …
Online reinforcement learning with sparse rewards through an active inference capsule
AD Noel, C Van Hoof, B Millidge - arXiv preprint arXiv:2106.02390, 2021 - arxiv.org
Intelligent agents must pursue their goals in complex environments with partial information
and often limited computational capacity. Reinforcement learning methods have achieved …
and often limited computational capacity. Reinforcement learning methods have achieved …
Optimizing Heat Alert Issuance for Public Health in the United States with Reinforcement Learning
Alerting the public when heat may harm their health is a crucial service, especially
considering that extreme heat events will be more frequent under climate change. Current …
considering that extreme heat events will be more frequent under climate change. Current …
DDoS and flash event detection in higher bandwidth SDN-IoT using multiagent reinforcement learning
The emergence of 5G, IoT, Big Data, and related technologies have necessitated a shift to
SDN architectural design and DRL algorithms for network task automation. Without prompt …
SDN architectural design and DRL algorithms for network task automation. Without prompt …
Q-FOX Learning: Breaking Tradition in Reinforcement Learning
Reinforcement learning (RL) is a subset of artificial intelligence (AI) where agents learn the
best action by interacting with the environment, making it suitable for tasks that do not …
best action by interacting with the environment, making it suitable for tasks that do not …