A survey of multi-task deep reinforcement learning
N Vithayathil Varghese, QH Mahmoud - Electronics, 2020 - mdpi.com
Driven by the recent technological advancements within the field of artificial intelligence
research, deep learning has emerged as a promising representation learning technique …
research, deep learning has emerged as a promising representation learning technique …
A survey on multi-task learning
Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …
leverage useful information contained in multiple related tasks to help improve the …
Hierarchical deep reinforcement learning with experience sharing for metaverse in education
R Hare, Y Tang - IEEE Transactions on Systems, Man, and …, 2022 - ieeexplore.ieee.org
Metaverse has gained increasing interest in education, with much of literature focusing on its
great potential to enhance both individual and social aspects of learning. However, little …
great potential to enhance both individual and social aspects of learning. However, little …
Lancon-learn: Learning with language to enable generalization in multi-task manipulation
Robots must be capable of learning from previously solved tasks and generalizing that
knowledge to quickly perform new tasks to realize the vision of ubiquitous and useful robot …
knowledge to quickly perform new tasks to realize the vision of ubiquitous and useful robot …
Solving large-scale pursuit-evasion games using pre-trained strategies
Pursuit-evasion games on graphs model the coordination of police forces chasing a fleeing
felon in real-world urban settings, using the standard framework of imperfect-information …
felon in real-world urban settings, using the standard framework of imperfect-information …
Robot skill learning and the data dilemma it faces: a systematic review
Purpose Compared with traditional methods relying on manual teaching or system
modeling, data-driven learning methods, such as deep reinforcement learning and imitation …
modeling, data-driven learning methods, such as deep reinforcement learning and imitation …
A hybrid multi-task learning approach for optimizing deep reinforcement learning agents
NV Varghese, QH Mahmoud - IEEE Access, 2021 - ieeexplore.ieee.org
Driven by recent technological advancements within the field of artificial intelligence (AI),
deep learning (DL) has been emerged as a promising representation learning technique …
deep learning (DL) has been emerged as a promising representation learning technique …
Grasper: A Generalist Pursuer for Pursuit-Evasion Problems
Pursuit-evasion games (PEGs) model interactions between a team of pursuers and an
evader in graph-based environments such as urban street networks. Recent advancements …
evader in graph-based environments such as urban street networks. Recent advancements …
Long-term planning with deep reinforcement learning on autonomous drones
U Ates - 2020 Innovations in Intelligent Systems and …, 2020 - ieeexplore.ieee.org
In this paper, we study a long-term planning scenario that is based on drone racing
competitions held in real life. We conducted this experiment on a framework created for …
competitions held in real life. We conducted this experiment on a framework created for …
Optimization of deep reinforcement learning with hybrid multi-task learning
NV Varghese, QH Mahmoud - 2021 IEEE International Systems …, 2021 - ieeexplore.ieee.org
As an outcome of the technological advancements occurred within artificial intelligence (AI)
domain in recent times, deep learning (DL) has been established its position as a prominent …
domain in recent times, deep learning (DL) has been established its position as a prominent …