attention, learn to solve routing problems! wouter kool ......2019/03/11 · 2 5/24/19 attention,...
TRANSCRIPT
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Attention, Learn to Solve Routing Problems!
Wouter Kool, Herke van Hoof, Max Welling
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5/24/192 Attention, Learn to Solve Routing Problems!
Travelling Scientist Problem (TSP)
InternationalConference on
LearningRepresentations2019
TSP* is (NP-)hard! * Travelling Salesman Problem (TSP)
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5/24/193 Attention, Learn to Solve Routing Problems!
What does it mean?
Finding optimal solutions for all problem instances
Finding acceptable solutions for relevant problem instances
* unless P = NP
‘next location should be nearby’We use HEURISTICSCan be seen as ‘rules of thumb’
MISSION:
IMPOSSIBLE
MISSION:
IMPOSSIBLE
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5/24/194 Attention, Learn to Solve Routing Problems!
Crafting of heuristics is similar to feature engineering
So what do we do?
HARD WORK Feature engineering
• Needs expert knowledge• Time consuming hand-tuning
Computer Vision Features(SIFT, etc.)
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5/24/195 Attention, Learn to Solve Routing Problems!
Crafting of heuristics is similar to feature engineering
Two eyes?
Nose?
Mouth?
It’s a face! It’s a face!
Traditional approachFeature engineering
Deep LearningNo feature engineering
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5/24/196 Attention, Learn to Solve Routing Problems!
Back to our problem
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Recurrent NeuralNetworks
5/24/197 Attention, Learn to Solve Routing Problems!
‘Translate’ problem into solution
Je suis une personneI am a person
(𝑥#, 𝑦#) (𝑥', 𝑦') (𝑥(, 𝑦() (𝑥), 𝑦)) (𝑥#, 𝑦#) (𝑥', 𝑦')(𝑥(, 𝑦() (𝑥), 𝑦))
FrenchDictionary
Idea
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5/24/198 Attention, Learn to Solve Routing Problems!
How does that work?
Sample 𝜋#~𝑝𝜽 𝜋# 𝑠) Sample 𝜋'~𝑝𝜽 𝜋' 𝑠, 𝜋#)
Instance 𝑠 =( 𝑥#, 𝑦# , 𝑥', 𝑦' , … , 𝑥1, 𝑦1 )
Solution 𝝅 = 𝜋#, 𝜋', …with length 𝐿 𝝅
Model 𝑝𝜽 𝜋4 𝑠, 𝜋54)=𝑝𝜽(next node | partial tour)
𝐸7𝜽(𝝅|9) 𝐿 𝝅How to optimize 𝜽?
Randomized algorithmwith expected cost:
Sample 𝜋4~𝑝𝜽 𝜋4 𝑠, 𝜋54)
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5/24/199 Attention, Learn to Solve Routing Problems!
Do something
Result = ?
Do more often! Do less often!
Sample 𝝅 ∼ 𝑝𝜽(; |𝑠)
𝐿 𝝅 = 7.43
Good! Bad!
Increase 𝑝𝜽(𝝅|𝑠) Decrease 𝑝𝜽(𝝅|𝑠)
We need a baselineto compare against: rollout earlier model
Repeat
𝝅@A ∼ 𝑝𝜽BC(; |𝑠) (greedy!)
𝐿 𝝅@A = 6.89
REINFORCE (for dummies)
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5/24/1910 Attention, Learn to Solve Routing Problems!
What’s the model architecture?
𝑝𝜽 𝜋4 𝑠, 𝜋54)
Graph convolutions
+
Read the paper…
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5/24/1911 Attention, Learn to Solve Routing Problems!
Experiments
Travelling Salesman Problem (TSP)
(Stochastic) Prize Collecting TSP ((S)PCTSP)
Vehicle Routing Problem (VRP)
Orienteering Problem (OP)
Minimize lengthVisit all nodes
Maximize total prizeMax length constraint
Minimize length + penalties of unvisited nodesCollect minimum total prize
Minimize lengthVisit all nodesTotal route demand must fit vehicle capacity
Train for each problem, same hyperparameters!
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5/24/1912 Attention, Learn to Solve Routing Problems!
Results Attention Model + Rollout Baseline
• Improves over classical heuristics!
• Improves over prior learned heuristics!• Attention Model improves• Rollout helps significantly
• Gets close to single-purpose SOTA (20 to 100 nodes)!• TSP 0.34% to 4.53% (greedy)• TSP 0.08% to 2.26% (best of 1280 samples)
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5/24/1913 Attention, Learn to Solve Routing Problems!
The end!
Thank you for your attention!
• Learning algorithms for optimization problems is promising
• Especially for less well-studied problems
• Can deal with uncertainty, specialize to data distribution, etc.