Advancing RAN Slicing with Offline Reinforcement Learning

• URL: http://arxiv.org/abs/2312.10547v1
• Date: Sat, 16 Dec 2023 22:09:50 GMT
• Title: Advancing RAN Slicing with Offline Reinforcement Learning
• Authors: Kun Yang, Shu-ping Yeh, Menglei Zhang, Jerry Sydir, Jing Yang, Cong Shen
• Abstract summary: This paper introduces offlineReinforcement Learning to solve the RAN slicing problem. We show how offline RL can effectively learn near-optimal policies from sub-optimal datasets. We also present empirical evidence of the efficacy of offline RL in adapting to various service-level requirements.
• Score: 15.259182716723496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic radio resource management (RRM) in wireless networks presents significant challenges, particularly in the context of Radio Access Network (RAN) slicing. This technology, crucial for catering to varying user requirements, often grapples with complex optimization scenarios. Existing Reinforcement Learning (RL) approaches, while achieving good performance in RAN slicing, typically rely on online algorithms or behavior cloning. These methods necessitate either continuous environmental interactions or access to high-quality datasets, hindering their practical deployment. Towards addressing these limitations, this paper introduces offline RL to solving the RAN slicing problem, marking a significant shift towards more feasible and adaptive RRM methods. We demonstrate how offline RL can effectively learn near-optimal policies from sub-optimal datasets, a notable advancement over existing practices. Our research highlights the inherent flexibility of offline RL, showcasing its ability to adjust policy criteria without the need for additional environmental interactions. Furthermore, we present empirical evidence of the efficacy of offline RL in adapting to various service-level requirements, illustrating its potential in diverse RAN slicing scenarios.

Related papers

• Preference Elicitation for Offline Reinforcement Learning [59.136381500967744]
  We propose Sim-OPRL, an offline preference-based reinforcement learning algorithm. Our algorithm employs a pessimistic approach for out-of-distribution data, and an optimistic approach for acquiring informative preferences about the optimal policy.
  arXiv  Detail & Related papers  (2024-06-26T15:59:13Z)
• Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets [13.22086908661673]
  Reinforcement learning (RL) has boosted the adoption of online RL for wireless radio resource management (RRM) Online RL algorithms require direct interactions with the environment. offline RL can produce a near-optimal RL policy even when all involved behavior policies are highly suboptimal.
  arXiv  Detail & Related papers  (2023-11-19T21:02:17Z)
• Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning [68.16998247593209]
  offline reinforcement learning (RL) paradigm provides recipe to convert static behavior datasets into policies that can perform better than the policy that collected the data. In this paper, we propose an adaptive scheme for action quantization. We show that several state-of-the-art offline RL methods such as IQL, CQL, and BRAC improve in performance on benchmarks when combined with our proposed discretization scheme.
  arXiv  Detail & Related papers  (2023-10-18T06:07:10Z)
• Leveraging Optimal Transport for Enhanced Offline Reinforcement Learning in Surgical Robotic Environments [4.2569494803130565]
  We introduce an innovative algorithm designed to assign rewards to offline trajectories, using a small number of high-quality expert demonstrations. This approach circumvents the need for handcrafted rewards, unlocking the potential to harness vast datasets for policy learning.
  arXiv  Detail & Related papers  (2023-10-13T03:39:15Z)
• Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning [66.43003402281659]
  A central question boils down to how to efficiently utilize online data collection to strengthen and complement the offline dataset. We design a three-stage hybrid RL algorithm that beats the best of both worlds -- pure offline RL and pure online RL. The proposed algorithm does not require any reward information during data collection.
  arXiv  Detail & Related papers  (2023-05-17T15:17:23Z)
• Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies [6.303272140868826]
  Reinforcement learning (RL) has shown great promise with algorithms learning in environments with large state and action spaces. Current deep RL algorithms require a tremendous amount of environment interactions for learning. offline RL algorithms try to address this issue by bootstrapping the learning process from existing logged data.
  arXiv  Detail & Related papers  (2022-12-15T20:36:10Z)
• FORLORN: A Framework for Comparing Offline Methods and Reinforcement Learning for Optimization of RAN Parameters [0.0]
  This paper introduces a new framework for benchmarking the performance of an RL agent in network environments simulated with ns-3. Within this framework, we demonstrate that an RL agent without domain-specific knowledge can learn how to efficiently adjust Radio Access Network (RAN) parameters to match offline optimization in static scenarios.
  arXiv  Detail & Related papers  (2022-09-08T12:58:09Z)
• Offline RL Policies Should be Trained to be Adaptive [89.8580376798065]
  We show that acting optimally in offline RL in a Bayesian sense involves solving an implicit POMDP. As a result, optimal policies for offline RL must be adaptive, depending not just on the current state but rather all the transitions seen so far during evaluation. We present a model-free algorithm for approximating this optimal adaptive policy, and demonstrate the efficacy of learning such adaptive policies in offline RL benchmarks.
  arXiv  Detail & Related papers  (2022-07-05T17:58:33Z)
• OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning [107.6943868812716]
  In many practical applications, the situation is reversed: an agent may have access to large amounts of undirected offline experience data, while access to the online environment is severely limited. Our main insight is that, when presented with offline data composed of a variety of behaviors, an effective way to leverage this data is to extract a continuous space of recurring and temporally extended primitive behaviors. In addition to benefiting offline policy optimization, we show that performing offline primitive learning in this way can also be leveraged for improving few-shot imitation learning.
  arXiv  Detail & Related papers  (2020-10-26T14:31:08Z)
• Critic Regularized Regression [70.8487887738354]
  We propose a novel offline RL algorithm to learn policies from data using a form of critic-regularized regression (CRR) We find that CRR performs surprisingly well and scales to tasks with high-dimensional state and action spaces.
  arXiv  Detail & Related papers  (2020-06-26T17:50:26Z)

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.