Results 91 to 100 of about 301,921 (265)
Learning to Walk Via Deep Reinforcement Learning
Robotics: Science and Systems XV, 2019 RSS 2019, https://sites.google.com/view/minitaur-locomotion/
Haarnoja, Tuomas +5 more
openaire +2 more sources
Advanced Functional Materials, EarlyView.Bioprosthetic aortic valves have revolutionized the treatment of aortic stenosis, but their durability is limited by structural valve deterioration (SVD). This review focuses on the pericardial tissue at the heart of these valves, examining how its mechanical properties and calcification drive fatigue and failure.
Gabriele Greco +7 more
wiley +1 more source
Deep Reinforcement Learning with Double Q-Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2016 The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whether they can generally be prevented. In this paper, we answer all these questions affirmatively.
van Hasselt, Hado +2 more
openaire +2 more sources
A Comprehensive Study on Reinforcement Learning and Deep Reinforcement Learning Schemes
Sir Syed University Research Journal of Engineering and TechnologyReinforcement learning (RL) has emerged as a powerful tool for creating artificial intelligence systems (AIS) and solving problems which require sequential decision-making. Reinforcement learning has achieved some impressive achievements in recent years,
Muhammad Azhar +4 more
doaj +1 more source
The advancements and applications of deep reinforcement learning in Go [PDF]
ITM Web of ConferencesCombining Deep Learning's perceptual skills with Reinforcement Learning's decision-making abilities, Deep Reinforcement Learning (DRL) represents a significant breakthrough in Artificial Intelligence (AI).
Zheng Xutao
doaj +1 more source
In Situ 3D Bioprinting: Impact of Cross‐Linking on the Adhesive Properties of Hydrogels
Advanced Functional Materials, EarlyView.In situ 3D bioprinting enables the direct deposition of cell‐laden, adhesive biomaterials for on‐site tissue regeneration. This review provides a comprehensive overview of how cross‐linking influences the bioadhesive properties of hydrogels used in 3D bioprinting, highlighting cross‐linking triggers, bioadhesion mechanisms, polymer interpenetration ...
Odile Romero Fernandez +4 more
wiley +1 more source
Advanced Functional Materials, EarlyView.We present a fully automated Bayesian optimization (BO) protocol for the parameterization of nonbonded interactions in coarse‐grain CG force fields (BACH). Using experimental thermophysical data, we apply the protocol to a broad range of liquids, spanning linear, branched, and unsaturated hydrocarbons, esters, triglycerides, and water.
Janak Prabhu +3 more
wiley +1 more source
Deep Reinforcement Learning for Swarm Systems
, 2019 Recently, deep reinforcement learning (RL) methods have been applied successfully to multi-agent scenarios. Typically, these methods rely on a concatenation of agent states to represent the information content required for decentralized decision making ...
Hüttenrauch, Maximilian +2 more
core
Engineering Strategies for Stable and Long‐Life Alkaline Zinc‐Based Flow Batteries
Advanced Functional Materials, EarlyView.Alkaline zinc‐based flow batteries face persistent challenges from unstable zinc deposition, including dendrite growth, passivation, corrosion, and hydrogen evolution, which severely limit cycling stability. Current research addresses these issues through coordinated electrode structuring, electrolyte regulation, and membrane design to control zinc ...
Yuran Bai +6 more
wiley +1 more source
Deep reinforcement learning from human preferences
, 2017 For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems.
Amodei, Dario +5 more
core