marl-based-constrained-optimization
Multi-Agent Reinforcement Learning for Distributed Constrained Optimal Resource Allocation
https://github.com/tensorman1/marl-based-constrained-optimization
Science Score: 44.0%
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Low similarity (6.9%) to scientific vocabulary
Repository
Multi-Agent Reinforcement Learning for Distributed Constrained Optimal Resource Allocation
Basic Info
- Host: GitHub
- Owner: Tensorman1
- License: mit
- Language: Python
- Default Branch: main
- Size: 75.2 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
This has been focused to make use of Multi-Agent Reinforcement Learning paradigms to approach the chance-constrained resource allocation problem in wireless networks. The repository will feature 4 programs - 1. Single Agent Q-Learning 2. Multi-Agent Q-Learning 3. Multi-Agent Deep Deterministic Policy Gradients 4. Stochastic Programming using Grid Search
The programs here are general purpose, from the perspective of wirelss communications. The underpinning principles are drawn from mobile communications with an alignment towards frequency division multiple access schemes. This framework may get extended to problems beyond wireless communications but application-specific heuristic modifications apply. If you have used all or some part of this repository and found it useful, please consider citing the repository using the citation instruction in the "Citation.md" file.
Owner
- Login: Tensorman1
- Kind: user
- Repositories: 1
- Profile: https://github.com/Tensorman1
Citation (Citation.md)
While this software is licensed under MIT and free to use, if you use these programs in your research or project, I kindly request that you cite this work as follows:
### BibTeX
@software{goswami_2025_wireless,
author = {Goswami, Arka Ian},
title = {Distributed Chance-Constrained Resource Allocation in Wireless Networks},
month = mar,
year = 2025,
version = {1.0.0},
publisher = {GitHub},
journal = {GitHub repository},
url = {https://github.com/Tensorman1/MARL-based-Constrained-Optimization}
}
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Last Year
- Watch event: 1
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