FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4324133630> ?p ?o ?g. }

• W4324133630 endingPage "16" @default.
• W4324133630 startingPage "1" @default.
• W4324133630 abstract "Quantum machine learning uses quantum mechanical concepts of superposition of states to make the decision. In this work, we used these quantum advantages to enhance deep reinforcement learning (DRL). Our primary and foremost goal is to investigate and elucidate a way of representing and solving the frozen lake problems by using PennyLane which contains Xanadu’s back-end quantum processing unit. This paper specifically discusses how to enhance classical deep reinforcement learning algorithms with quantum computing technology, making quantum agents get a maximum reward after a fixed number of epochs and realizing the effect of a number of variational quantum layers on the trainability of enhanced framework. We have analyzed that, as the number of layers increases, the ability of the quantum agent to converge to the optimal state also increases. For this work, we have trained the framework agent with 2, 3, and 5 variational quantum layers. An agent with 2 layers converges to a total reward of 0.95 after the training episode of 526. The other agent with layers converges to a total reward of 0.95 after the training episode of 397 and the agent which uses 5 quantum variational layers converges to a total reward of 0.95 after the training episode of 72. From this, we can understand that the agent with a more variational layer exploits more and converges to the optimal state before the other agent. We also analyzed our work in terms of different learning rate hyperparameters. We recorded every single learning epoch to demonstrate the outcomes of enhanced DRL algorithms with selected 0.1, 0.2, 0.3, and 0.4 learning rates or alpha values. From this result, we can conclude that the greater the learning rate values in quantum deep reinforcement learning, the fewer timesteps it takes to move from the start point to the goal state." @default.
• W4324133630 created "2023-03-15" @default.
• W4324133630 creator A5012548460 @default.
• W4324133630 creator A5013182572 @default.
• W4324133630 date "2023-03-14" @default.
• W4324133630 modified "2023-10-14" @default.
• W4324133630 title "Investigating the Effects of Hyperparameters in Quantum-Enhanced Deep Reinforcement Learning" @default.
• W4324133630 cites W1502029943 @default.
• W4324133630 cites W1994630055 @default.
• W4324133630 cites W2117941808 @default.
• W4324133630 cites W2145339207 @default.
• W4324133630 cites W2521267242 @default.
• W4324133630 cites W2559394418 @default.
• W4324133630 cites W2580674237 @default.
• W4324133630 cites W2781738013 @default.
• W4324133630 cites W2888774813 @default.
• W4324133630 cites W2903712026 @default.
• W4324133630 cites W2921125426 @default.
• W4324133630 cites W2959720248 @default.
• W4324133630 cites W2964327027 @default.
• W4324133630 cites W3038667240 @default.
• W4324133630 cites W3045093737 @default.
• W4324133630 cites W3090652612 @default.
• W4324133630 cites W3093944484 @default.
• W4324133630 cites W3098780233 @default.
• W4324133630 cites W3103436470 @default.
• W4324133630 cites W3133779704 @default.
• W4324133630 cites W3145589299 @default.
• W4324133630 cites W3160771574 @default.
• W4324133630 cites W3161932608 @default.
• W4324133630 cites W3186445007 @default.
• W4324133630 cites W3188362739 @default.
• W4324133630 cites W3195637163 @default.
• W4324133630 cites W3209429312 @default.
• W4324133630 cites W4251942288 @default.
• W4324133630 doi "https://doi.org/10.1155/2023/2451990" @default.
• W4324133630 hasPublicationYear "2023" @default.
• W4324133630 type Work @default.
• W4324133630 citedByCount "3" @default.
• W4324133630 countsByYear W43241336302023 @default.
• W4324133630 crossrefType "journal-article" @default.
• W4324133630 hasAuthorship W4324133630A5012548460 @default.
• W4324133630 hasAuthorship W4324133630A5013182572 @default.
• W4324133630 hasBestOaLocation W43241336301 @default.
• W4324133630 hasConcept C121332964 @default.
• W4324133630 hasConcept C126255220 @default.
• W4324133630 hasConcept C134306372 @default.
• W4324133630 hasConcept C154945302 @default.
• W4324133630 hasConcept C15706264 @default.
• W4324133630 hasConcept C27753989 @default.
• W4324133630 hasConcept C2779094486 @default.
• W4324133630 hasConcept C33923547 @default.
• W4324133630 hasConcept C41008148 @default.
• W4324133630 hasConcept C55615164 @default.
• W4324133630 hasConcept C58053490 @default.
• W4324133630 hasConcept C62520636 @default.
• W4324133630 hasConcept C84114770 @default.
• W4324133630 hasConcept C8642999 @default.
• W4324133630 hasConcept C97541855 @default.
• W4324133630 hasConceptScore W4324133630C121332964 @default.
• W4324133630 hasConceptScore W4324133630C126255220 @default.
• W4324133630 hasConceptScore W4324133630C134306372 @default.
• W4324133630 hasConceptScore W4324133630C154945302 @default.
• W4324133630 hasConceptScore W4324133630C15706264 @default.
• W4324133630 hasConceptScore W4324133630C27753989 @default.
• W4324133630 hasConceptScore W4324133630C2779094486 @default.
• W4324133630 hasConceptScore W4324133630C33923547 @default.
• W4324133630 hasConceptScore W4324133630C41008148 @default.
• W4324133630 hasConceptScore W4324133630C55615164 @default.
• W4324133630 hasConceptScore W4324133630C58053490 @default.
• W4324133630 hasConceptScore W4324133630C62520636 @default.
• W4324133630 hasConceptScore W4324133630C84114770 @default.
• W4324133630 hasConceptScore W4324133630C8642999 @default.
• W4324133630 hasConceptScore W4324133630C97541855 @default.
• W4324133630 hasLocation W43241336301 @default.
• W4324133630 hasOpenAccess W4324133630 @default.
• W4324133630 hasPrimaryLocation W43241336301 @default.
• W4324133630 hasRelatedWork W2040965095 @default.
• W4324133630 hasRelatedWork W2121062711 @default.
• W4324133630 hasRelatedWork W2321097323 @default.
• W4324133630 hasRelatedWork W3051329393 @default.
• W4324133630 hasRelatedWork W3093944484 @default.
• W4324133630 hasRelatedWork W4233714475 @default.
• W4324133630 hasRelatedWork W4308022458 @default.
• W4324133630 hasRelatedWork W4319449194 @default.
• W4324133630 hasRelatedWork W4324133630 @default.
• W4324133630 hasRelatedWork W4385011780 @default.
• W4324133630 hasVolume "2023" @default.
• W4324133630 isParatext "false" @default.
• W4324133630 isRetracted "false" @default.
• W4324133630 workType "article" @default.