Results 121 to 130 of about 492,986 (210)

Contextual Bandits with Stochastic Experts

CoRR, 2018
20 pages, 2 Figures, Accepted for publication in AISTATS ...
Sen, Rajat, Shanmugam, Karthikeyan, Sharma, Nihal, Shakkottai, Sanjay   +3 more
openaire   +3 more sources

Cognitive Digital Twins for Smart City Mobility: An Adaptive Multi-Agent System Approach

IEEE Access
Digital Twins have become a central tool for Smart City planning and management, enabling simulation and analysis of complex urban systems such as mobility and energy infrastructures. However, most existing implementations primarily focus on physical and
Elio Masciari, Enea Vincenzo Napolitano
doaj   +1 more source

On Quantum Natural Policy Gradients

IEEE Transactions on Quantum Engineering
This article delves into the role of the quantum Fisher information matrix (FIM) in enhancing the performance of parameterized quantum circuit (PQC)-based reinforcement learning agents.
Andre Sequeira, Luis Paulo Santos, Luis Soares Barbosa   +2 more
doaj   +1 more source

Adaptive Edge Caching and Routing in Named Data Networking of Things Using Contextual Bandits and SDN Controllers

IEEE Access
Named Data Networking is a promising architecture for the Internet of Things, since its in-network caching can hold content close to consumers and shorten retrieval delay.
Albatool A. Alhawas, Saad Al-Ahmadi, Achraf Gazdar   +2 more
doaj   +1 more source

PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits

, 2018
We address the problem of regret minimization in logistic contextual bandits, where a learner decides among sequential actions or arms given their respective contexts to maximize binary rewards.
Feng, Karen, Dumitrascu, Bianca, Engelhardt, Barbara   +2 more
core  

Nonstochastic Contextual Combinatorial Bandits.

, 2023
We study a contextual version of online combinatorial optimisation with full and semi-bandit feedback. In this sequential decision-making problem, an online learner has to select an action from a combinatorial decision space after seeing a vector-valued context in each round.
L. Zierahn, D. van der Hoeven, N. Cesa Bianchi, G. Neu   +3 more
openaire   +1 more source

Contextual Bandits with Neural Networks and Trees

Πολλά προβλήματα περιλαμβάνουν τη λήψη διαδοχικών αποφάσεων υπό συν- θήκες αβεβαιότητας, όπου πρέπει να εξισορροπηθεί η εξερεύνηση (exploration) με την εκμετάλλευση (exploitation). Τα bandits παρέχουν ένα απλό μοντέλο για αυτό το δίλημμα.
ΚΩΝΣΤΑΝΤΑΤΟΣ ΚΩΝΣΤΑΝΤΙΝΟΣ, KONSTANTATOS KONSTANTINOS   +1 more
core   +1 more source

Blockchain and RL-Based Secured Task Offloading Framework for Software-Defined 5G Edge Networks

IEEE Access
5G (Fifth Generation) technology represents a significant advancement in telecommunications, facilitating industry innovation and advancement for applications across various sectors.
N. Sethu Subramanian, Prabhakar Krishnan, Kurunandan Jain, K. B. Aneesh Kumar, Tulika Pandey, Rajkumar Buyya   +5 more
doaj   +1 more source

Calibrated Recommendations with Contextual Bandits

CoRR
Spotify's Home page features a variety of content types, including music, podcasts, and audiobooks. However, historical data is heavily skewed toward music, making it challenging to deliver a balanced and personalized content mix. Moreover, users' preference towards different content types may vary depending on the time of day, the day of week, or even
Diego Feijer, Himan Abdollahpouri, Sanket Gupta, Alexander Clare, Yuxiao Wen, Todd Wasson, Maria Dimakopoulou, Zahra Nazari, Kyle Kretschman, Mounia Lalmas   +9 more
openaire   +3 more sources

Feel-Good Thompson Sampling for Contextual Dueling Bandits

Contextual dueling bandits, where a learner compares two options based on context and receives feedback indicating which was preferred, extends classic dueling bandits by incorporating contextual information for decision-making and preference learning ...
Li, Xuheng, Zhao, Heyang, Gu, Quanquan
core