Achieving Regular and Fair Learning in Combinatorial Multi-Armed Bandit
Author:
Affiliation:
1. Pennsylvania State University,Department of Electrical Engineering,University Park,PA,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10621050/10621073/10621191.pdf?arnumber=10621191
Reference42 articles.
1. CBMoS: Combinatorial Bandit Learning for Mode Selection and Resource Allocation in D2D Systems
2. Optimal Routing to Parallel Servers With Unknown Utilities—Multi-Armed Bandit With Queues
3. Learning Multiuser Channel Allocations in Cognitive Radio Networks: A Combinatorial Multi-Armed Bandit Formulation
4. Multiuser Scheduling in Centralized Cognitive Radio Networks: A Multi-Armed Bandit Approach
5. Low-Complexity Learning for Dynamic Spectrum Access in Multi-User Multi-Channel Networks
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