interaction networks for learning about objects, relations and physics
TRANSCRIPT
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Interaction Networks for Learning about Objects,Relations and Physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, koray kavukcuoglu (Google DeepMind)
NIPS 2016 Reading ClubPresenter: Ken Kuroki (@enuroi)
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Background & Purpose
• Some attempts to learn physical dynamics so far. (rigid bodies, fluid dynamics, 3D trajectory etc.)
• This study aims to construct a general-purpose learnable physics engine. (that can learn novel physical systems)
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Model at a Glance
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O1
O2
O1,t O2,t r
fR
et+1
O2,t
fO
et+1
O2,t+1
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Model in Detail 1
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Rr =0 0 1 1 0 0
Rs =1 0 0 0 0 1
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Model in Detail 2
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NR : number of relationsNO : number of objectsbk : <oi, oj, rk> (rearranges the objects and relations into interaction terms)
Relatione: multiple for one object c: aggregated by a
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Implementation 1
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O = Ds
NO
R = NR
NO
NR
NO
Rr Rs
receiver sender
DR
NR
Ra
attributes
, ,
object1's status vector
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Implementation 2
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m(G) = Ds
Ds
DR
NR
ORr
ORs
Ra
= B[b1, b2, ..., bk]
[e1, e2, ..., ek] = E
fR
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Implementation 3
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G, X, EE = ERr– T
[O; X; E] = C–
Ds
Ds
DR
NR
O
X
E–
fR
a
P = Ot+1
DA
fA
(Free energy)
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Architecture• MLP (bias, ReLU)
By hyperparamerter search...
• FR : four 150-length hidden layers, output length 50
• FO : one 100-length hidden layer, output length 2 (x and y velocity)
• FA : one 25-length hidden layer
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Optimization
• Used AdamLearning rate 0.001, and downscaled by *0.8 for 40 epochs
• L2 regularization (penalty factor by grid search)
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Training
Simulated 2000 scenes over 1000 time steps
• Training : 1 million sample, for 2000 epochs (mini-batches of 100 to balance distributions)
• Validation : 200k sample
• Test data : 200k sample
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Experiments
1. N-body
2. Bouncing balls
3. String
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Comparison
Alternative Models:
1. Constant velocity (output=input)
2. MLP (two 300-length hidden layers) input: flattened vector of all the input data
3. Interaction Network without E (interaction)
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Results
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Discussion
1. Performed better than alternatives
2. Baseline MLP couldn't effectively learn interaction
3. To understand "intuitive physics engine" in human
4. Potential to expand the model
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Presenter's Comments
1. Can be applied to a larger system? (time & memory-wise)
2. Probably it can be parallelized
3. Really advantageous to alternatives?
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