a question

Hi,

first thank you for your repo!

I just want to ask you, is there a source from where I can understand more about the architecture of your work, besides the mentioned paper? I mean, maybe you wrote a blog post about your algorithm or something like this.

Kind regards, Paddy

update:

Also, could you please tell how I can reuse the model which I’ve trained. I mean I finished the training and want to apply it for sorting a list, f.e.:

python model.py [3,4,6,5,1,2]

And the output would be a sorted list:

[1,2,3,4,5,6]

[pemami4911]

@unnir Yep! That’s the general framework. The critic helps to reduce the variance when updating the actor (policy) parameters

@ricgama I contacted the authors of Learning Combinatorial Optimization Algorithms Over Graphs, as they present some results comparing this to their proposed algorithm, and got the following reply about their attempt to replicate the results

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“We also use single GPU card for PN-AC (as Hanjun mentioned we use K80 for experiments). It is indeed painful to make Bello et.al.’s method work on TSP2D. We carefully read their paper for many times, and used all the tricks they mentioned. We also found it not working at all, and tried to use exponential moving average as the critic, instead of a separate critic neural network. Finally it works (the loss goes down), but not as good as the performance reported in their paper.”

[pemami4911]

Hmm… I would just recommend using the reward function I have in tsp_task.py, which is the one from the paper. Basically just average tour length. With gradient clipping, it shouldn’t cause any funny business during backprop. Check out issue #5 though-- it turns out our issue was not cloning the mask!