A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applications
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910724887937024 |
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| author | Vangaru, Sriniketh Rosen, Daniel Green, Dylan Rodriguez, Raphael Wiecek, Maxwell Johnson, Amos Jones, Alyse M. Headley, William C. |
| author_facet | Vangaru, Sriniketh Rosen, Daniel Green, Dylan Rodriguez, Raphael Wiecek, Maxwell Johnson, Amos Jones, Alyse M. Headley, William C. |
| contents | Technological trends show that Radio Frequency Reinforcement Learning (RFRL) will play a prominent role in the wireless communication systems of the future. Applications of RFRL range from military communications jamming to enhancing WiFi networks. Before deploying algorithms for these purposes, they must be trained in a simulation environment to ensure adequate performance. For this reason, we previously created the RFRL Gym: a standardized, accessible tool for the development and testing of reinforcement learning (RL) algorithms in the wireless communications space. This environment leveraged the OpenAI Gym framework and featured customizable simulation scenarios within the RF spectrum. However, the RFRL Gym was limited to training a single RL agent per simulation; this is not ideal, as most real-world RF scenarios will contain multiple intelligent agents in cooperative, competitive, or mixed settings, which is a natural consequence of spectrum congestion. Therefore, through integration with Ray RLlib, multi-agent reinforcement learning (MARL) functionality for training and assessment has been added to the RFRL Gym, making it even more of a robust tool for RF spectrum simulation. This paper provides an overview of the updated RFRL Gym environment. In this work, the general framework of the tool is described relative to comparable existing resources, highlighting the significant additions and refactoring we have applied to the Gym. Afterward, results from testing various RF scenarios in the MARL environment and future additions are discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_21521 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applications Vangaru, Sriniketh Rosen, Daniel Green, Dylan Rodriguez, Raphael Wiecek, Maxwell Johnson, Amos Jones, Alyse M. Headley, William C. Machine Learning Artificial Intelligence Multiagent Systems Networking and Internet Architecture Technological trends show that Radio Frequency Reinforcement Learning (RFRL) will play a prominent role in the wireless communication systems of the future. Applications of RFRL range from military communications jamming to enhancing WiFi networks. Before deploying algorithms for these purposes, they must be trained in a simulation environment to ensure adequate performance. For this reason, we previously created the RFRL Gym: a standardized, accessible tool for the development and testing of reinforcement learning (RL) algorithms in the wireless communications space. This environment leveraged the OpenAI Gym framework and featured customizable simulation scenarios within the RF spectrum. However, the RFRL Gym was limited to training a single RL agent per simulation; this is not ideal, as most real-world RF scenarios will contain multiple intelligent agents in cooperative, competitive, or mixed settings, which is a natural consequence of spectrum congestion. Therefore, through integration with Ray RLlib, multi-agent reinforcement learning (MARL) functionality for training and assessment has been added to the RFRL Gym, making it even more of a robust tool for RF spectrum simulation. This paper provides an overview of the updated RFRL Gym environment. In this work, the general framework of the tool is described relative to comparable existing resources, highlighting the significant additions and refactoring we have applied to the Gym. Afterward, results from testing various RF scenarios in the MARL environment and future additions are discussed. |
| title | A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applications |
| topic | Machine Learning Artificial Intelligence Multiagent Systems Networking and Internet Architecture |
| url | https://arxiv.org/abs/2410.21521 |