Tensorflow Implementation of Interaction Networks from Google Deepmind.
Implementation is on Tensorflow r1.2.
https://arxiv.org/abs/1612.00222
"Reasoning about objects, relations, and physics is central to human intelligence, and a key goal of artificial intelligence. Here we introduce the interaction network, a model which can reason about how objects in complex systems interact, supporting dynamical predictions, as well as inferences about the abstract properties of the system." From the paper
python interaction_network.py
Data are gathered from implemented physics engine (physics_engine.py), which by using given the number of objects initializes and processes random weights[0.2-9kg], distance[10-100m] and angle[0-360] same as the paper settings. Currently orbit system is not implemented yet, the experiments on here are gone with not orbit system (no central biggest object).
Almost settings are same to the paper, which is clearly written for that except bellowed.
For state, there no description, and I used 5-D vector [mass,x_position,y_position,x_velocity,y_velocity]. For R_a and X, there are no description for n-body test, thus I used zero-array with D_R=1, D_X=1. For D_P, descriptions in page 6 are different, (for f_O, D_P=2, and for \phi_A, D_P=10) in my implementation, I used D_P=2 (x and y velocities).
Except above three things, other settings are same to paper as followed.
For \phi_R function, 4 hidden layers MLP with 150 nodes, ReLU activation function for hidden layers, linear ones for output layer and D_E=50 settings are used. For \phi_O function, 1 hidden layers MLP with 100 nodes, ReLU for hidden layer, linear for output layer and D_P=2 settings are used. Adam Optimizer is used with 0.001 learning rate. L2-regularization is used for matrix E and all parameters.
Currently, in progress of training, validation mse is less than 0.05. That is better than the paper results, for which, I assume state or D_P settings are different to original ones.
After finishing training, I will upload exact results with qualititive ones.