Hello everyone,
I am working on a deep learning power control project using NetSim. So far, I have successfully implemented a tabular Q-learning algorithm and obtained positive results. Now, I am looking to implement advanced reinforcement learning algorithms such as actor-critic and proximal policy optimization (PPO) using Gymnasium.
Could anyone provide guidance on how to interface NetSim with Gymnasium (https://github.com/Farama-Foundation/Gymnasium) for applying these RL algorithms? Any examples or resources would be greatly appreciated.
Thank you!
There are two parts:
1. NetSim code is in C
2. Gym/SB-3 code would be in Python.
Use a socket to pass data between C and Python
You can also see these discussions
- https://www.researchgate.net/post/How_can_I_apply_Reinforcement_learning_for_5G
- https://www.researchgate.net/post/Applying_Machine_learning_in_5G_simulations
You can look at https://tetcos.com/pdf/v14.1/5G-DL-Power-Control-to-Maximize-Sum-Throughput-Using-RL_v14.1.pdf. This project has some examples involving PPO and A2C by interfacing with Gymnasium environment for these advanced RL algorithms.
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