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地质科技通报, 2021 The use of microbially induced carbonate precipitation (MICP) technology to improve the cementation quality of oil and gas well cementing has attracted more and more attention in recent years.
Tianle Liu +5 more
doaj +1 more source
Deep Reinforcement Learning: A Brief Survey [PDF]
IEEE Signal Processing Magazine, 2017 Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a step toward building autonomous systems with a higherlevel understanding of the visual world.
Kai Arulkumaran +3 more
semanticscholar +1 more source
IEEE Access, 2021 A well-designed demand response (DR) program is essential in smart home to optimize energy usage according to user preferences. In this study, we proposed a multiobjective reinforcement learning (MORL) algorithm to design a DR program.
Song-Jen Chen, Wei-Yu Chiu, Wei-Jen Liu
doaj +1 more source
Rainbow: Combining Improvements in Deep Reinforcement Learning [PDF]
AAAI Conference on Artificial Intelligence, 2017 The deep reinforcement learning community has made several independent improvements to the DQN algorithm. However, it is unclear which of these extensions are complementary and can be fruitfully combined. This paper examines six extensions to the DQN
Matteo Hessel +9 more
semanticscholar +1 more source
Experimental Study on Axial Compression of an Insulating Layer through a Composite Shear Wall
Advances in Civil Engineering, 2021 Based on the research of composite walls at home and abroad, a construction method of continuous opening of the insulation layer in the specimen is proposed.
Yuliang Wang, Congcong Wang, Zhixing Cao
doaj +1 more source
Learning an Accurate State Transition Dynamics Model by Fitting Both a Function and its Derivative
IEEE Access, 2022 Learning accurate state transition dynamics model in a sample-efficient way is important to predict the future states from the current states and actions of a system both accurately and efficiently in model-based reinforcement learning for many robotic ...
Youngho Kim, Hoosang Lee, Jeha Ryu
doaj +1 more source
Experience-weighted Attraction Learning in Normal Form Games [PDF]
, 1999 In ‘experience-weighted attraction’ (EWA) learning, strategies have attractions that reflect initial predispositions, are updated based on payoff experience, and determine choice probabilities according to some rule (e.g., logit).
Camerer, Colin F., Ho, Teck Hua
core +2 more sources
Effect of Gr Contents on Wear Properties of Al2024/MgO/Al2O3/Gr Hybrid Composites [PDF]
, 2018 In the present study, hybrid metal matrix composites, Al2024/10Al2O3, Al2024/10Al2O3/3MgO, Al2024/10Al2O3/6MgO, Al2024/10Al2O3/3MgO/1.5 Gr, Al2024/10Al2O3/3MgO/3Gr, and reinforcement samples (AA 2024) produced with powder metallurgy process.
Albayrak, S. +3 more
core +1 more source
Construction of aggregation operators with noble reinforcement [PDF]
, 2007 This paper examines disjunctive aggregation operators used in various recommender systems. A specific requirement in these systems is the property of noble reinforcement: allowing a collection of high-valued arguments to reinforce each other while ...
Beliakov, Gleb, Calvo, Tomasa
core +1 more source
The Journal of Neuroscience, 2021 Humans routinely learn the value of actions by updating their expectations based on past outcomes – a process driven by reward prediction errors (RPEs). Importantly, however, implementing a course of action also requires the investment of effort. Recent work has revealed a close link between the neural signals involved in effort exertion and those ...
Huw Jarvis +5 more
openaire +3 more sources