A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access
Open accessAbstract
To make efficient use of limited spectral resources, we in this work propose a deep actor-critic reinforcement learning based framework for dynamic multichannel access. We consider both a single-user case and a scenario in which multiple users attempt to access channels simultaneously. We employ the proposed framework as a single agent in the single-user case, and extend it to a decentralized multi-agent framework in the multi-user scenario. In both cases, we develop algorithms for the actor-critic deep reinforcement learning and evaluate the proposed learning policies via experiments and numerical results. In the single-user model, in order to evaluate the performance of the proposed channel access policy and the framework's tolerance against uncertainty, we explore different channel switching patterns and different switching probabilities. In the case of multiple users, we analyze the probabilities of each user accessing channels with favorable channel conditions and the probability of collision. We also address a time-varying environment to identify the adaptive ability of the proposed framework. Additionally, we provide comparisons (in terms of both the average reward and time efficiency) between the proposed actor-critic deep reinforcement learning framework, Deep-Q network (DQN) based approach, random access, and the optimal policy when the channel dynamics are known.
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The challenge with deep actor criticism is that both actor and critic need stable targets.
“The challenge with deep actor criticism is that both actor and critic need stable targets.”
From Deep reinforcement learning-based adaptive fuzzy control for electro-hydraulic servo system (2025) · cites this paper as [16]
It requires consistently training the critic without divergence that delays learning and the propagation of value estimations through the target network.
“It requires consistently training the critic without divergence that delays learning and the propagation of value estimations through the target network [16].”
From Deep reinforcement learning-based adaptive fuzzy control for electro-hydraulic servo system (2025) · cites this paper as [16]
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- Authors
- Zhong, C., Lu, Z., Gursoy, M. C., & Velipasalar, S.
- Affiliations
- Syracuse University (US)
- Venue
- IEEE Transactions on Cognitive Communications and Networking
- Published
- 2019-12
- Volume
- 5
- Issue
- 4
- Pages
- 1125-1139
- ISSN
- 2332-7731 (ISSN-L)
- 2372-2045
- OpenAlex
- W2983432303
- Cited by
- 123
- References
- 37
- Citations by year
- 2026: 11 · 2025: 15 · 2024: 20 · 2023: 20 · 2022: 21 · 2021: 26 · 2020: 9 · 2019: 1
- FWCI
- 7.80
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Cognitive Communications and Networking (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Networks and Protocols
- Physical Sciences › Computer Science › Computer Networks and Communications › Cognitive Radio Networks and Spectrum Sensing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Keywords
- channel switching, multi-agent reinforcement learning, collision probability, deep Q-network comparison, random access, cognitive radio
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