Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks.

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Deep Learning FDRL-PC-Dyspan
Overview

FDRL-PC-Dyspan

Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks.

This repository contains the entire code for our work "Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks" and has been accepted for presentation in IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) 2021. You can find the paper here: https://arxiv.org/abs/2112.03465

Requirements

The following versions have been tested: Python 3.7.11 + Pytorch 1.7.0 But newer versions should also be fine.

The introduction of each file

Environment and benchmarks:

Environment_CU.py: the mutilcell cellular wireless Environment simulator for power control.

Benchmark_alg.py: bench mark class which contains 4 algorithms: WWMSE, FP, random and maxpower.

Benchmark_test.py: testing the benchmark performance in an environment.

The wireless Environment simulator and Banchmark algorithms were taken from this repository: https://github.com/mengxiaomao/PA_TWC

Value Badesed DRL, DQN:

DQN_agent_pytorch.py: The DQN agent class.

DeepQN_Model.py: Deep Q netwrork architechure for the DQN agent class.

Experience_replay.py: Exprience replar buffer class for DQN agent.

main_dqn.py: Centrelized Deep Q Learning main file.

main_dqn_multiagent.py: Federated and Distributed multi agent Deep Q Learning main file.

Policy Badesed DRL, DPG:

Reinforce_Pytorch.py：Deep Reinforce agent and the policy netwrok architecture.

main_reinforce.py: Centrelized Deep Policy Gradient (Deep Reinforce) main file.

main_Policy_multiagent.py: Federated and Distributed multi agent Deep policy gradient main file.

Plots and Reults:

plot_fig4.py: Plotting the Figure 4 of the paper.

optmization_DRL_compare_all.py: Compaering the performance of all methods (Table 1 of the paper).

Actor Critic Based DRL: (These were not used for the paper)

ddpg_agent.py：Deep Deterministic Plocy gradient (DDPG) agent class.

TD3.py: Twin Delayed DDPG (TD3) agent class.

main_ddpg.py: Main file for train the TD3 and DDPG agents.

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Comments
  • About the parameter

    About the parameter "state_num"

    Hello！ I was reading your article lately, but I didn't quite understand a little setting in this code. I would like to ask why the parameter “self.C” in the file Environment_CU.py has a value of 16? And the state_num set 3*16+2=50?I am new to this field and sincerely hope to hear from you！Thank you！

    opened by hexing2333 0
Owner
Peyman Tehrani
PhD Student @ UC Irvine
Peyman Tehrani
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