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# Interaction Networks for Learning about Objects, Relations and Physics

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For my presentation for a reading group. I have not in any way contributed this study, which is done by the researchers named on the first slide.

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### Interaction Networks for Learning about Objects, Relations and Physics

1. 1. 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 Club  Presenter: Ken Kuroki (@enuroi) 1
2. 2. 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) 2
3. 3. Model at a Glance 3 O1 O2 O1,t O2,t r fR et+1 O2,t fO et+1 O2,t+1
4. 4. Model in Detail 1 4 Rr = 0 0 1 1 0 0 Rs = 1 0 0 0 0 1
5. 5. Model in Detail 2 5 NR : number of relations NO : number of objects bk : <oi, oj, rk>  (rearranges the objects and relations into interaction terms) Relation  e: multiple for one object c: aggregated by a
6. 6. Implementation 1 6 O = Ds NO R = NR NO NR NO Rr Rs receiver sender DR NR Ra attributes , , object1's status vector
7. 7. Implementation 2 7 m(G) = Ds Ds DR NR ORr ORs Ra = B [b1, b2, ..., bk] [e1, e2, ..., ek] = E fR
8. 8. Implementation 3 8 G, X, E E = ERr – T [O; X; E] = C – Ds Ds DR NR O X E – fR a P = Ot+1 DA fA (Free energy)
9. 9. 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 9
10. 10. Optimization • Used Adam  Learning rate 0.001, and downscaled by *0.8 for 40 epochs • L2 regularization  (penalty factor by grid search) 10
11. 11. 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 11
12. 12. Experiments 1. N-body 2. Bouncing balls 3. String 12
13. 13. 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) 13
14. 14. Results 14
15. 15. 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 15
16. 16. 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? 16