This is the code repository for AAAI 2024 paper TAPE: Leveraging Agent Topology for Cooperative Multi-Agent Policy Gradient.
This repo is built upon SMAC, DOP and PAC.
First install SMAC, then
for stochastic TAPE,
cd stochastic
bash runalgo.sh for deterministic TAPE,
cd deterministic
bash runalgo.shpkill -u ($youruser) python
pkill -u ($youruser) Main_ThreadYou can use networkx package to generate graphs and use the adjcency matrix for the agent topology
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
# Create a Watts-Strogatz small-world network
n = 20
k = 4
p = 0.1
G = nx.watts_strogatz_graph(n, k, p)
# plot
nx.draw(G, with_labels=True)
plt.show()
A = nx.adjacency_matrix(G)
for i in range(n):
A[i,i]=1
print(A.todense())Please cite
@article{lou2023tape,
title={TAPE: Leveraging Agent Topology for Cooperative Multi-Agent Policy Gradient},
author={Lou, Xingzhou and Zhang, Junge and Norman, Timothy J and Huang, Kaiqi and Du, Yali},
journal={arXiv preprint arXiv:2312.15667},
year={2023}
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent