RL-QN: A Reinforcement Learning Framework for Optimal Control of Queueing Systems

• URL: http://arxiv.org/abs/2011.07401v2
• Date: Thu, 7 Apr 2022 17:48:09 GMT
• Title: RL-QN: A Reinforcement Learning Framework for Optimal Control of Queueing Systems
• Authors: Bai Liu, Qiaomin Xie, Eytan Modiano
• Abstract summary: We consider using model-based reinforcement learning (RL) to learn the optimal control policy for queueing networks. Traditional approaches in RL, however, cannot handle the unbounded state spaces of the network control problem. We propose a new algorithm, called Reinforcement Learning for Queueing Networks (RL-QN), which applies model-based RL methods over a finite subset of the state space.
• Score: 8.611328447624677
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid advance of information technology, network systems have become increasingly complex and hence the underlying system dynamics are often unknown or difficult to characterize. Finding a good network control policy is of significant importance to achieve desirable network performance (e.g., high throughput or low delay). In this work, we consider using model-based reinforcement learning (RL) to learn the optimal control policy for queueing networks so that the average job delay (or equivalently the average queue backlog) is minimized. Traditional approaches in RL, however, cannot handle the unbounded state spaces of the network control problem. To overcome this difficulty, we propose a new algorithm, called Reinforcement Learning for Queueing Networks (RL-QN), which applies model-based RL methods over a finite subset of the state space, while applying a known stabilizing policy for the rest of the states. We establish that the average queue backlog under RL-QN with an appropriately constructed subset can be arbitrarily close to the optimal result. We evaluate RL-QN in dynamic server allocation, routing and switching problems. Simulation results show that RL-QN minimizes the average queue backlog effectively.

Related papers

• Continuous Control with Coarse-to-fine Reinforcement Learning [15.585706638252441]
  We present a framework that trains RL agents to zoom-into a continuous action space in a coarse-to-fine manner. We introduce a concrete, value-based algorithm within the framework called Coarse-to-fine Q-Network (CQN) CQN robustly learns to solve real-world manipulation tasks within a few minutes of online training.
  arXiv  Detail & Related papers  (2024-07-10T16:04:08Z)
• Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning [69.00997996453842]
  We propose a deep Reinforcement Learning approach to learn a joint Admission Control and Resource Allocation policy for virtual network embedding. We show that HRL-ACRA outperforms state-of-the-art baselines in terms of both the acceptance ratio and long-term average revenue.
  arXiv  Detail & Related papers  (2024-06-25T07:42:30Z)
• Learning RL-Policies for Joint Beamforming Without Exploration: A Batch Constrained Off-Policy Approach [1.0080317855851213]
  We consider the problem of network parameter cancellation optimization for networks. We show that deploying an algorithm in the real world for exploration and learning can be achieved with the data without exploring.
  arXiv  Detail & Related papers  (2023-10-12T18:36:36Z)
• Single-Shot Pruning for Offline Reinforcement Learning [47.886329599997474]
  Deep Reinforcement Learning (RL) is a powerful framework for solving complex real-world problems. One way to tackle this problem is to prune neural networks leaving only the necessary parameters. We close the gap between RL and single-shot pruning techniques and present a general pruning approach to the Offline RL.
  arXiv  Detail & Related papers  (2021-12-31T18:10:02Z)
• Text Generation with Efficient (Soft) Q-Learning [91.47743595382758]
  Reinforcement learning (RL) offers a more flexible solution by allowing users to plug in arbitrary task metrics as reward. We introduce a new RL formulation for text generation from the soft Q-learning perspective. We apply the approach to a wide range of tasks, including learning from noisy/negative examples, adversarial attacks, and prompt generation.
  arXiv  Detail & Related papers  (2021-06-14T18:48:40Z)
• Better than the Best: Gradient-based Improper Reinforcement Learning for Network Scheduling [60.48359567964899]
  We consider the problem of scheduling in constrained queueing networks with a view to minimizing packet delay. We use a policy gradient based reinforcement learning algorithm that produces a scheduler that performs better than the available atomic policies.
  arXiv  Detail & Related papers  (2021-05-01T10:18:34Z)
• Smart Scheduling based on Deep Reinforcement Learning for Cellular Networks [18.04856086228028]
  We propose a smart scheduling scheme based on deep reinforcement learning (DRL) We provide implementation-friend designs, i.e., a scalable neural network design for the agent and a virtual environment training framework. We show that the DRL-based smart scheduling outperforms the conventional scheduling method and can be adopted in practical systems.
  arXiv  Detail & Related papers  (2021-03-22T02:09:16Z)
• Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning [56.17667147101263]
  In real-world tasks, reinforcement learning agents encounter situations that are not present during training time. To ensure reliable performance, the RL agents need to exhibit robustness against worst-case situations. We propose the Robust Hallucinated Upper-Confidence RL (RH-UCRL) algorithm to provably solve this problem.
  arXiv  Detail & Related papers  (2021-03-18T16:50:17Z)
• Proactive and AoI-aware Failure Recovery for Stateful NFV-enabled Zero-Touch 6G Networks: Model-Free DRL Approach [0.0]
  We propose a model-free deep reinforcement learning (DRL)-based proactive failure recovery framework called zero-touch PFR (ZT-PFR) ZT-PFR is for the embedded stateful virtual network functions (VNFs) in network function virtualization (NFV) enabled networks.
  arXiv  Detail & Related papers  (2021-02-02T21:40:35Z)
• Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning [40.5603189901241]
  We investigate a radio access network (RAN) slicing problem for Internet of vehicles (IoV) services with different quality of service (QoS) requirements. A dynamic RAN slicing framework is presented to dynamically allocate radio spectrum and computing resource. We show that the RAWS effectively reduces the system cost while satisfying requirements with a high probability, as compared with benchmarks.
  arXiv  Detail & Related papers  (2020-12-03T15:08:38Z)
• EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL [48.552287941528]
  Off-policy reinforcement learning holds the promise of sample-efficient learning of decision-making policies. In the offline RL setting, standard off-policy RL methods can significantly underperform. We introduce Expected-Max Q-Learning (EMaQ), which is more closely related to the resulting practical algorithm.
  arXiv  Detail & Related papers  (2020-07-21T21:13:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.