Results 31 to 40 of about 1,056,132 (188)
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations [PDF]
Robotics: Science and Systems Conference, 2017 Dexterous multi-fingered hands are extremely versatile and provide a generic way to perform multiple tasks in human-centric environments. However, effectively controlling them remains challenging due to their high dimensionality and large number of ...
A. Rajeswaran +5 more
semanticscholar +1 more source
Tongxin xuebao, 2023 To solve the problem that intelligent devices equipped with deep reinforcement learning agents lack effective security data sharing mechanisms in the intelligent Internet of things, a general federated reinforcement learning (GenFedRL) framework was ...
Biao JIN +4 more
doaj +2 more sources
Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review
Robotics, 2021 Deep learning has provided new ways of manipulating, processing and analyzing data. It sometimes may achieve results comparable to, or surpassing human expert performance, and has become a source of inspiration in the era of artificial intelligence ...
Rongrong Liu +4 more
doaj +1 more source
Hyperbolic Deep Reinforcement Learning
CoRR, 2022 Preprint
Edoardo Cetin +3 more
openaire +3 more sources
Target‐driven visual navigation in indoor scenes using reinforcement learning and imitation learning
CAAI Transactions on Intelligence Technology, 2022 Here, the challenges of sample efficiency and navigation performance in deep reinforcement learning for visual navigation are focused and a deep imitation reinforcement learning approach is proposed.
Qiang Fang +3 more
doaj +1 more source
Off-policy Maximum Entropy Deep Reinforcement Learning Algorithm Based on RandomlyWeighted Triple Q -Learning [PDF]
Jisuanji kexue, 2022 Reinforcement learning is an important branch of machine learning.With the development of deep learning,deep reinforcement learning research has gradually developed into the focus of reinforcement learning research.Model-free off-policy deep ...
FAN Jing-yu, LIU Quan
doaj +1 more source
Crowd-Robot Interaction: Crowd-Aware Robot Navigation With Attention-Based Deep Reinforcement Learning [PDF]
IEEE International Conference on Robotics and Automation, 2018 Mobility in an effective and socially-compliant manner is an essential yet challenging task for robots operating in crowded spaces. Recent works have shown the power of deep reinforcement learning techniques to learn socially cooperative policies ...
Changan Chen +3 more
semanticscholar +1 more source
Magnetic control of tokamak plasmas through deep reinforcement learning
Nature, 2022 Nuclear fusion using magnetic confinement, in particular in the tokamak configuration, is a promising path towards sustainable energy. A core challenge is to shape and maintain a high-temperature plasma within the tokamak vessel.
Jonas Degrave +30 more
semanticscholar +1 more source
Abstraction for Deep Reinforcement Learning
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022 We characterise the problem of abstraction in the context of deep reinforcement learning. Various well established approaches to analogical reasoning and associative memory might be brought to bear on this issue, but they present difficulties because of the need for end-to-end differentiability.
Murray Shanahan, Melanie Mitchell
openaire +2 more sources
Deep Reinforcement and InfoMax Learning
CoRR, 2020 NeurIPS ...
Bogdan Mazoure +4 more
openaire +3 more sources