Deep reinforcement learning in smart manufacturing: A review and prospects
To facilitate the personalized smart manufacturing paradigm with cognitive automation
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …
Machine learning in aerodynamic shape optimization
Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …
optimization (ASO), thanks to the availability of aerodynamic data and continued …
The rise and potential of large language model based agents: A survey
For a long time, humanity has pursued artificial intelligence (AI) equivalent to or surpassing
the human level, with AI agents considered a promising vehicle for this pursuit. AI agents are …
the human level, with AI agents considered a promising vehicle for this pursuit. AI agents are …
A survey on data‐efficient algorithms in big data era
A Adadi - Journal of Big Data, 2021 - Springer
The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately,
many application domains do not have access to big data because acquiring data involves a …
many application domains do not have access to big data because acquiring data involves a …
Deep reinforcement learning in production systems: a systematic literature review
Shortening product development cycles and fully customisable products pose major
challenges for production systems. These not only have to cope with an increased product …
challenges for production systems. These not only have to cope with an increased product …
Learning for a robot: Deep reinforcement learning, imitation learning, transfer learning
J Hua, L Zeng, G Li, Z Ju - Sensors, 2021 - mdpi.com
Dexterous manipulation of the robot is an important part of realizing intelligence, but
manipulators can only perform simple tasks such as sorting and packing in a structured …
manipulators can only perform simple tasks such as sorting and packing in a structured …
Multi-agent deep reinforcement learning for multi-robot applications: A survey
J Orr, A Dutta - Sensors, 2023 - mdpi.com
Deep reinforcement learning has produced many success stories in recent years. Some
example fields in which these successes have taken place include mathematics, games …
example fields in which these successes have taken place include mathematics, games …
State2explanation: Concept-based explanations to benefit agent learning and user understanding
As more non-AI experts use complex AI systems for daily tasks, there has been an
increasing effort to develop methods that produce explanations of AI decision making that …
increasing effort to develop methods that produce explanations of AI decision making that …
An AR-assisted Deep Reinforcement Learning-based approach towards mutual-cognitive safe human-robot interaction
With the emergence of Industry 5.0, the human-centric manufacturing paradigm requires
manufacturing equipment (robots, etc.) interactively assist human workers to deal with …
manufacturing equipment (robots, etc.) interactively assist human workers to deal with …
Autonomous tracking using a swarm of UAVs: A constrained multi-agent reinforcement learning approach
In this paper, we aim to design an autonomous tracking system for a swarm of unmanned
aerial vehicles (UAVs) to localize a radio frequency (RF) mobile target. In the system, UAVs …
aerial vehicles (UAVs) to localize a radio frequency (RF) mobile target. In the system, UAVs …