Robust reinforcement learning: A review of foundations and recent advances
Reinforcement learning (RL) has become a highly successful framework for learning in
Markov decision processes (MDP). Due to the adoption of RL in realistic and complex …
Markov decision processes (MDP). Due to the adoption of RL in realistic and complex …
Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook
In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
Deep reinforcement learning for indoor mobile robot path planning
This paper proposes a novel incremental training mode to address the problem of Deep
Reinforcement Learning (DRL) based path planning for a mobile robot. Firstly, we evaluate …
Reinforcement Learning (DRL) based path planning for a mobile robot. Firstly, we evaluate …
AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2
The drug repurposing of known approved drugs (eg, lopinavir/ritonavir) has failed to treat
SARS-CoV-2-infected patients. Therefore, it is important to generate new chemical entities …
SARS-CoV-2-infected patients. Therefore, it is important to generate new chemical entities …
Learning mobile manipulation through deep reinforcement learning
Mobile manipulation has a broad range of applications in robotics. However, it is usually
more challenging than fixed-base manipulation due to the complex coordination of a mobile …
more challenging than fixed-base manipulation due to the complex coordination of a mobile …
Designing reinforcement learning algorithms for digital interventions: pre-implementation guidelines
Online reinforcement learning (RL) algorithms are increasingly used to personalize digital
interventions in the fields of mobile health and online education. Common challenges in …
interventions in the fields of mobile health and online education. Common challenges in …
Review of deep reinforcement learning approaches for conflict resolution in air traffic control
Z Wang, W Pan, H Li, X Wang, Q Zuo - Aerospace, 2022 - mdpi.com
Deep reinforcement learning (DRL) has been widely adopted recently for its ability to solve
decision-making problems that were previously out of reach due to a combination of …
decision-making problems that were previously out of reach due to a combination of …
Autodrive: A comprehensive, flexible and integrated digital twin ecosystem for autonomous driving research & education
Prototyping and validating hardware–software components, sub-systems and systems within
the intelligent transportation system-of-systems framework requires a modular yet flexible …
the intelligent transportation system-of-systems framework requires a modular yet flexible …
Recent advances in deep reinforcement learning applications for solving partially observable markov decision processes (pomdp) problems: Part 1—fundamentals …
X Xiang, S Foo - Machine Learning and Knowledge Extraction, 2021 - mdpi.com
The first part of a two-part series of papers provides a survey on recent advances in Deep
Reinforcement Learning (DRL) applications for solving partially observable Markov decision …
Reinforcement Learning (DRL) applications for solving partially observable Markov decision …
Deep reinforcement learning for truck-drone delivery problem
Utilizing drones for delivery is an effective approach to enhancing delivery efficiency and
lowering expenses. However, to overcome the delivery range and payload capacity …
lowering expenses. However, to overcome the delivery range and payload capacity …