Learning to Schedule Network Resources Throughput and Delay Optimally Using Q+-Learning
- Title
- Learning to Schedule Network Resources Throughput and Delay Optimally Using Q+-Learning
- Author
- 이주현
- Issue Date
- 2021-01
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Citation
- IEEE-ACM TRANSACTIONS ON NETWORKING, v. 29, Issue. 2, Page. 750-763
- Abstract
- As network architecture becomes complex and the user requirement gets diverse, the role of efficient network resource management becomes more important. However, existing throughput-optimal scheduling algorithms such as the max-weight algorithm suffer from poor delay performance. In this paper, we present reinforcement learning-based network scheduling algorithms for a single-hop downlink scenario which achieve throughput-optimality and converge to minimal delay. To this end, we first formulate the network optimization problem as a Markov decision process (MDP) problem. Then, we introduce a new state-action value function called Q + -function and develop a reinforcement learning algorithm called Q + -learning with UCB (Upper Confidence Bound) exploration which guarantees small performance loss during a learning process. We also derive an upper bound of the sample complexity in our algorithm, which is more efficient than the best known bound from Q-learning with UCB exploration by a factor of γ 2 where γ is the discount factor of the MDP problem. Finally, via simulation, we verify that our algorithm shows a delay reduction of up to 40.8% compared to the max-weight algorithm over various scenarios. We also show that the Q + -learning with UCB exploration converges to an ε-optimal policy 10 times faster than Q-learning with UCB.
- URI
- https://ieeexplore.ieee.org/abstract/document/9336288https://repository.hanyang.ac.kr/handle/20.500.11754/167026
- ISSN
- 1063-6692; 1558-2566
- DOI
- 10.1109/TNET.2021.3051663
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML