Awesome

Graph-based Deterministic Policy Gradient for Repetitive Combinatorial Optimization Problems

This repository is part of the paper with the given title, that has been published in ICLR 2023 https://openreview.net/forum?id=yHIIM9BgOo. This README contains instructions for replicating the 5 numerical experiments demonstrated in the paper. The code is based on Python3 + Tensorflow 2.

Updates on 1/24/2024:

[1/24/2024] replaced lr with learning_rate in the code to avoid unstability in training caused by incorrect learning rate in later versions of Tensorflow 2.
[1/24/2024] added the training and validation datasets used in MWIS example.
[1/24/2024] the output activation of the actor network can be changed from relu to leaky_relu or linear in order to ease the training.
[3/6/2024] add exploration based on uniform noise to stablize training for MWIS and MWDS
[3/6/2024] on-demand memory allocation for servers with multiple GPUs
[3/6/2024] minor code optimization: move gradient computing outside GradientTape scope and create bash scripts files from README.

Abstract

We propose an actor-critic framework for graph-based machine learning pipelines with non-differentiable blocks, and apply it to repetitive combinatorial optimization problems (COPs) under hard constraints. Repetitive COP refers to problems to be solved repeatedly on graphs of the same or slowly changing topology but rapidly changing node or edge weights. Compared to one-shot COPs, repetitive COPs often rely on fast heuristics to solve one instance of the problem before the next one arrives, at the cost of a relatively large optimality gap. Through numerical experiments on several discrete optimization problems, we show that our approach can learn reusable node or edge representations to reduce the optimality gap of fast heuristics for independent repetitive COPs, and can even enable the system to account for dependencies between consecutive COPs to optimize long-term objectives.

Setup

The following intructions assume that Python3.9 is your default Python3. Other versions of Python3 may also work.

pip3 install -r requirements.txt

Install any missing packages while running the code or notebook.

1. Maximum Weighted Independent Set (MWIS)

Trained models

GDPG-Twin ./model/result_GCNTwinAlt_deep_ld1_c32_l3_cheb1_diver1_mwis_dqn

GDPG-Twin (critic twin network) ./model/result_GCNTwinAlt_deep_ld1_c32_l3_cheb1_diver1_mwis_critic

ZOO ./model/result_GCNZoo4_deep_ld1_c32_l3_cheb1_diver1_mwis_dqn

Ad hoc RL ./model/result_IS4SAT_deep_ld1_c32_l3_cheb1_diver1_mwis_dqn

Generating training and testing datasets

cd .. # To the upper directory of the root of this project
git clone https://github.com/zhongyuanzhao/distgcn.git
cd distgcn
bash bash/run_data_generation.sh

copy generated datasets from ../distgcn/data/ to ./data/ of this project.

See https://github.com/zhongyuanzhao/distgcn for more information.

1.1 GDPG-Twin

setval='MWISTwin'
layers=3
echo "MWIS GDPG-Twin"
echo "Training starts"
python3 mwis_gcn_train_twin.py --training_set=${setval} --epsilon=1.0 --epsilon_min=0.002 --gamma=0.99 --feature_size=1 --diver_num=1 --datapath=./data/ER_Graph_Uniform_mixN_mixp_train0 --test_datapath=./data/ER_Graph_Uniform_GEN21_test1 --max_degree=1 --predict=mwis --learning_rate=0.0001 --learning_decay=1.0 --hidden1=32 --num_layer=3 --epochs=25 --ntrain=1 ;
echo "Testing starts"
python3 mwis_test_complexity.py --training_set=${setval} --epsilon=1 --epsilon_min=0.002 --feature_size=1 --diver_num=1 --datapath=./data/ER_Graph_Uniform_GEN21_test2 --max_degree=1 --predict=mwis --learning_rate=0.00001 --hidden1=32 --num_layer=3 --opt=0 ;
echo "MWIS Twin done"

1.2 Zeroth order optimization

setval='MWISZOO'
layers=3
echo "MWIS ZOO"
echo "Training starts"
python3 mwis_gcn_train_zoo.py --training_set=${setval} --epsilon=1.0 --epsilon_min=0.002 --gamma=0.99 --feature_size=1 --diver_num=1 --datapath=./data/ER_Graph_Uniform_mixN_mixp_train0 --test_datapath=./data/ER_Graph_Uniform_GEN21_test1 --max_degree=1 --predict=mwis --learning_rate=0.0001 --learning_decay=1.0 --hidden1=32 --num_layer=3 --epochs=25 --ntrain=1 ;
echo "Testing starts"
python3 mwis_test_complexity.py --training_set=${setval} --epsilon=1 --epsilon_min=0.002 --feature_size=1 --diver_num=1 --datapath=./data/ER_Graph_Uniform_GEN21_test2 --max_degree=1 --predict=mwis --learning_rate=0.00001 --hidden1=32 --num_layer=3 --opt=0 ;
echo "MWIS ZOO done"

1.3 Ad hoc Reinforcement Learning

cd ../distgcn/
layers=3
setval='MWISAdhoc'
echo "MWIS ad hoc RL"
echo "Training starts"
python3 mwis_dqn_origin.py --training_set=${setval}  --epsilon=1 --epsilon_min=0.002 --feature_size=1 --diver_num=1 --datapath=./data/ER_Graph_Uniform_mixN_mixp_train0 --test_datapath=./data/ER_Graph_Uniform_GEN21_test1 --max_degree=1 --predict=mwis --learning_rate=0.00001 --hidden1=32 --num_layer=${layers} --epochs=5
python3 mwis_dqn_origin.py --training_set=${setval}  --epsilon=0.2 --epsilon_min=0.002 --feature_size=1 --diver_num=1 --datapath=./data/ER_Graph_Uniform_mixN_mixp_train0 --test_datapath=./data/ER_Graph_Uniform_GEN21_test1 --max_degree=1 --predict=mwis --learning_rate=0.00001 --hidden1=32 --num_layer=${layers} --epochs=5
python3 mwis_dqn_origin.py --training_set=${setval}  --epsilon=0.1 --epsilon_min=0.002 --feature_size=1 --diver_num=1 --datapath=./data/ER_Graph_Uniform_mixN_mixp_train0 --test_datapath=./data/ER_Graph_Uniform_GEN21_test1 --max_degree=1 --predict=mwis --learning_rate=0.000001 --hidden1=32 --num_layer=${layers} --epochs=5
python3 mwis_dqn_origin.py --training_set=${setval}  --epsilon=0.05 --epsilon_min=0.002 --feature_size=1 --diver_num=1 --datapath=./data/ER_Graph_Uniform_mixN_mixp_train0 --test_datapath=./data/ER_Graph_Uniform_GEN21_test1 --max_degree=1 --predict=mwis --learning_rate=0.0000001 --hidden1=32 --num_layer=${layers} --epochs=10

echo "Testing starts"

testfolder20="ER_Graph_Uniform_GEN21_test2";
testfolder22="BA_Graph_Uniform_GEN21_test2";

dist='Uniform'
python3 mwis_dqn_test.py --training_set=${setval} --epsilon=.0002 --feature_size=1 --diver_num=1 --datapath=./data/${testfolder20} --max_degree=1 --predict=mwis --learning_rate=0.00001 --hidden1=32 --num_layer=${layers} --epochs=10
mv ./output/result_${setval}_deep_ld1_c32_l${layers}_cheb1_diver1_mwis_dqn.csv ./output/result_${setval}_deep_ld1_c32_l${layers}_cheb1_diver1_mwis_dqn_${testfolder20}.csv

python3 mwis_dqn_test.py --training_set=${setval} --epsilon=.0002 --feature_size=1 --diver_num=1 --datapath=./data/${testfolder22} --max_degree=1 --predict=mwis --learning_rate=0.00001 --hidden1=32 --num_layer=${layers} --epochs=10
mv ./output/result_${setval}_deep_ld1_c32_l${layers}_cheb1_diver1_mwis_dqn.csv ./output/result_${setval}_deep_ld1_c32_l${layers}_cheb1_diver1_mwis_dqn_${testfolder22}.csv

echo "MWIS ad hoc RL done"

See https://github.com/zhongyuanzhao/distgcn for more information.

2. Minimum Weighted Dominating Set (MWDS)

Trained models

Actor

./model/result_GCNDSGDYER_deep_ld1_c32_l5_cheb1_diver1_mpy_dpg_policy

Critic

./model/result_GCNDSGDYER_deep_ld1_c32_l5_cheb1_diver1_mpy_critic

Make sure lines 46 - 48 in mwds_gcn_call_twin.py are as follows

# heuristic_func = mwds_greedy_mis
heuristic_func = mwds_greedy
# heuristic_func = mwds_vvv

2.1 GDPG-Twin

setval="GCNDSGDY"
echo "MWDS training starts"
python3 mwds_gcn_train_twin.py --training_set=${setval} --epsilon=1.0 --epsilon_min=0.002 --gamma=0.99 --feature_size=1 --diver_num=1 --datapath=./data/ --test_datapath=./data/ER_Graph_Uniform_GEN21_test1 --max_degree=1 --predict=mpy --learning_rate=0.0001 --learning_decay=1.0 --hidden1=32 --num_layer=5 --epochs=20 --ntrain=1 --gtype='er'
echo "MWDS test starts"
python3 mwds_gcn_test.py --training_set=${setval} --epsilon=1.0 --epsilon_min=0.002 --gamma=0.99 --feature_size=1 --diver_num=1 --datapath=./data/ --test_datapath=./data/WS_Graph_Uniform_GEN21_test1 --max_degree=1 --predict=mpy --learning_rate=0.0001 --learning_decay=1.0 --hidden1=32 --num_layer=5 --epochs=20 --ntrain=1 --gtype='ws'
python3 mwds_gcn_test.py --training_set=${setval} --epsilon=1.0 --epsilon_min=0.002 --gamma=0.99 --feature_size=1 --diver_num=1 --datapath=./data/ --test_datapath=./data/BA_Graph_Uniform_GEN21_test1 --max_degree=1 --predict=mpy --learning_rate=0.0001 --learning_decay=1.0 --hidden1=32 --num_layer=5 --epochs=20 --ntrain=1 --gtype='ba'
python3 mwds_gcn_test.py --training_set=${setval} --epsilon=1.0 --epsilon_min=0.002 --gamma=0.99 --feature_size=1 --diver_num=1 --datapath=./data/ --test_datapath=./data/ER_Graph_Uniform_GEN21_test1 --max_degree=1 --predict=mpy --learning_rate=0.0001 --learning_decay=1.0 --hidden1=32 --num_layer=5 --epochs=20 --ntrain=1 --gtype='er'
python3 mwds_gcn_test.py --training_set=${setval} --epsilon=1.0 --epsilon_min=0.002 --gamma=0.99 --feature_size=1 --diver_num=1 --datapath=./data/ --test_datapath=./data/GRP_Graph_Uniform_GEN21_test1 --max_degree=1 --predict=mpy --learning_rate=0.0001 --learning_decay=1.0 --hidden1=32 --num_layer=5 --epochs=20 --ntrain=1 --gtype='grp'

3. Node Weighted Steiner Tree (NWST)

Trained models

Actor

./model/result_GCNSteinerGRP_deep_ld1_c32_l5_cheb1_diver1_mpy_dpg_policy

Critic

./model/result_GCNSteinerGRP_deep_ld1_c32_l5_cheb1_diver1_mpy_critic

3.1 GDPG-Twin

setval="GCNSTGRP"
echo "NWST training starts"
python3 steiner_gcn_train_twin.py --training_set=${setval} --epsilon=1.0 --epsilon_min=0.002 --gamma=0.99 --feature_size=1 --diver_num=1 --datapath=./data/ --test_datapath=./data/ER_Graph_Uniform_GEN21_test1 --max_degree=1 --predict=mpy --learning_rate=0.0001 --learning_decay=1.0 --hidden1=32 --num_layer=5 --epochs=20 --ntrain=1 --gtype='grp'
echo "NWST test starts"
python3 steiner_gcn_test.py --training_set=${setval} --epsilon=1.0 --epsilon_min=0.002 --gamma=0.99 --feature_size=1 --diver_num=1 --datapath=./data/ --test_datapath=./data/WS_Graph_Uniform_GEN21_test1 --max_degree=1 --predict=mpy --learning_rate=0.0001 --learning_decay=1.0 --hidden1=32 --num_layer=5 --epochs=20 --ntrain=1 --gtype='ws'
python3 steiner_gcn_test.py --training_set=${setval} --epsilon=1.0 --epsilon_min=0.002 --gamma=0.99 --feature_size=1 --diver_num=1 --datapath=./data/ --test_datapath=./data/BA_Graph_Uniform_GEN21_test1 --max_degree=1 --predict=mpy --learning_rate=0.0001 --learning_decay=1.0 --hidden1=32 --num_layer=5 --epochs=20 --ntrain=1 --gtype='ba'
python3 steiner_gcn_test.py --training_set=${setval} --epsilon=1.0 --epsilon_min=0.002 --gamma=0.99 --feature_size=1 --diver_num=1 --datapath=./data/ --test_datapath=./data/ER_Graph_Uniform_GEN21_test1 --max_degree=1 --predict=mpy --learning_rate=0.0001 --learning_decay=1.0 --hidden1=32 --num_layer=5 --epochs=20 --ntrain=1 --gtype='er'
python3 steiner_gcn_test.py --training_set=${setval} --epsilon=1.0 --epsilon_min=0.002 --gamma=0.99 --feature_size=1 --diver_num=1 --datapath=./data/ --test_datapath=./data/GRP_Graph_Uniform_GEN21_test1 --max_degree=1 --predict=mpy --learning_rate=0.0001 --learning_decay=1.0 --hidden1=32 --num_layer=5 --epochs=20 --ntrain=1 --gtype='grp'

4. Minimum Weighted Connected Dominating Set (MWCDS)

Trained models

Actor

./model/result_GCNCDS_deep_ld1_c32_l5_cheb1_diver2_mpy_dpg_policy

Critic

./model/result_GCNCDS_deep_ld1_c32_l5_cheb1_diver2_mpy_critic

Make sure lines 46 - 48 in mwcds_gcn_call_twin.py are as follows

heuristic_func = dist_greedy_mwcds
# heuristic_func = mwcds_vvv
# heuristic_func = greedy_mwcds2

4.1 GDPG-Twin

setval="GCNCDSMIS"
echo "MWCDS training starts"
python3 mwcds_gcn_train_twin.py --training_set=${setval} --epsilon=1.0 --epsilon_min=0.002 --gamma=0.99 --feature_size=1 --diver_num=2 --datapath=./data/ --test_datapath=./data/ER_Graph_Uniform_GEN21_test1 --max_degree=1 --predict=mpy --learning_rate=0.0001 --learning_decay=1.0 --hidden1=32 --num_layer=5 --epochs=20 --ntrain=1 --gtype='grp'
echo "MWCDS test starts"
python3 mwcds_gcn_test.py --training_set=${setval} --epsilon=1.0 --epsilon_min=0.002 --gamma=0.99 --feature_size=1 --diver_num=2 --datapath=./data/ --test_datapath=./data/WS_Graph_Uniform_GEN21_test1 --max_degree=1 --predict=mpy --learning_rate=0.0001 --learning_decay=1.0 --hidden1=32 --num_layer=5 --epochs=20 --ntrain=1 --gtype='ws'
python3 mwcds_gcn_test.py --training_set=${setval} --epsilon=1.0 --epsilon_min=0.002 --gamma=0.99 --feature_size=1 --diver_num=2 --datapath=./data/ --test_datapath=./data/BA_Graph_Uniform_GEN21_test1 --max_degree=1 --predict=mpy --learning_rate=0.0001 --learning_decay=1.0 --hidden1=32 --num_layer=5 --epochs=20 --ntrain=1 --gtype='ba'
python3 mwcds_gcn_test.py --training_set=${setval} --epsilon=1.0 --epsilon_min=0.002 --gamma=0.99 --feature_size=1 --diver_num=2 --datapath=./data/ --test_datapath=./data/ER_Graph_Uniform_GEN21_test1 --max_degree=1 --predict=mpy --learning_rate=0.0001 --learning_decay=1.0 --hidden1=32 --num_layer=5 --epochs=20 --ntrain=1 --gtype='er'
python3 mwcds_gcn_test.py --training_set=${setval} --epsilon=1.0 --epsilon_min=0.002 --gamma=0.99 --feature_size=1 --diver_num=2 --datapath=./data/ --test_datapath=./data/GRP_Graph_Uniform_GEN21_test1 --max_degree=1 --predict=mpy --learning_rate=0.0001 --learning_decay=1.0 --hidden1=32 --num_layer=5 --epochs=20 --ntrain=1 --gtype='grp'

5. Delay-oriented distributed link scheduling

Trained models

GDPG-Twin

Actor ./model/result_GDPGsr_deep_ld1_c32_l1_cheb1_diver1_mis_gdpg

Critic ./model/result_GDPGsr_deep_ld1_c32_l1_cheb1_diver1_mis_gdpg_crt

Lookahead RL (Ref)

./model/result_STARBA2_deep_ld1_c32_l1_cheb1_diver1_mis_exp

Quick test for Figure 5

bash bash/wireless_gcn_delay_test_twin.sh
bash bash/wireless_gcn_delay_test.sh

5.1 GDPG-Twin

Train

python3 wireless_gcn_train_delay_twin.py --wt_sel=qr --load_min=0.05 --load_max=0.05 --load_step=0.002 --feature_size=1 --epsilon=0.09 --epsilon_min=0.001 --diver_num=1 --datapath=./data/BA_Graph_Uniform_mixN_mixp_train0 --test_datapath=./data/BA_Graph_Uniform_GEN21_test2 --max_degree=1 --predict=mis --hidden1=32 --num_layer=1 --instances=2 --training_set=DelayTwin --opt=0 --gamma=0.95 --learning_rate=0.001 --graph=ba2

Test

Change line 3 in bash/wireless_gcn_delay_test_twin.sh to setval='DelayTwin', then run bash bash/wireless_gcn_delay_test_twin.sh.

Note that when --graph=ba2, the datasets specified by datapath and test_datapath are not used, the training graphs are generated on the fly.

5.2 Lookahead Reinforcement Learning

Train

python3 wireless_gcn_train_delay.py --wt_sel=qr --load_min=0.05 --load_max=0.05 --load_step=0.002 --feature_size=1 --epsilon=0.09 --epsilon_min=0.001 --diver_num=1 --datapath=./data/BA_Graph_Uniform_mixN_mixp_train0 --test_datapath=./data/BA_Graph_Uniform_GEN21_test2 --max_degree=1 --predict=mis --hidden1=32 --num_layer=1 --instances=2 --training_set=DelayLHRL --opt=0 --gamma=0.9 --learning_rate=0.0001 --graph=ba2

see https://github.com/zhongyuanzhao/gcn-dql for more information.

Change line 3 in bash/wireless_gcn_delay_test.sh to setval='DelayLHRL', then run bash bash/wireless_gcn_delay_test.sh.

Plot results

Figures 1-4 plot_test_results.ipynb,
Figure 5 plot_delay_oriented_twin.py,
Figure 6 plot_training.ipynb.

6. Core References

6.1 Major Codebases

https://github.com/zhongyuanzhao/distgcn
Delay-oriented distributed link scheduling https://github.com/zhongyuanzhao/gcn-dql
Graph convolutional neural network: 1) spektral https://graphneural.network/, 2) ./gcn/ https://github.com/tkipf/gcn

6.2 Algorithms

Maximum Weighted Independent Set (MWIS): local greedy solver [Joo 2012], code [Zhao 2021]
Minimum Weighted Dominating Set (MWDS) [Greedy heuristic in Jovanovic 2010]
Node Weighted Steiner Tree (NWST) [SPH: Takahashi 1990, K-SPH Bauer 1996]
Minimum Weighted Connected Dominating Set (MWCDS) [Sun 2019]
Zeroth order optimization (ZOO) [Liu 2020]

Awesome

Graph-based Deterministic Policy Gradient for Repetitive Combinatorial Optimization Problems

Abstract

Setup

Directory

1. Maximum Weighted Independent Set (MWIS)

1.1 GDPG-Twin

1.2 Zeroth order optimization

1.3 Ad hoc Reinforcement Learning

2. Minimum Weighted Dominating Set (MWDS)

2.1 GDPG-Twin

3. Node Weighted Steiner Tree (NWST)

3.1 GDPG-Twin

4. Minimum Weighted Connected Dominating Set (MWCDS)

4.1 GDPG-Twin

5. Delay-oriented distributed link scheduling

5.1 GDPG-Twin

5.2 Lookahead Reinforcement Learning

Plot results

6. Core References

6.1 Major Codebases

6.2 Algorithms