Again a rejected paper due to lack of innovation and SOTA result.
In OpenReview#4 wrote:
On the basis of state of the art performance at solving the TSP using learned methods:
The model requires access to a dataset with optimal solutions to train it, and I doubt it can solve the problems faster than Gurobi in terms of wall time. For this result to be more interesting, the authors should be able to show that the model can generalize to larger problems (where the combinatorial complexity may start making approaches like Gurobi struggle). However it is not clear if the model can generalize to larger graphs.
Beyond that I am not an expert on TSP specifically, and I don’t know the TSP literature, so I cannot give a strong recommendation.
There are some additional papers that may be relevant to this line of work:
(MIP, NeurIPS 2019) Learning to branch in MIP problems using similar technique pretraining a GNN and use it to guide a solver at test time (no MCTS though) (https://arxiv.org/abs/1906.01629)
(SAT, SAT Conference 2019) Learning to predict unsat cores (similar to the previous one but for SAT problems) (https://arxiv.org/abs/1903.04671)
(Structural construction, ICML 2019) Building graphs by choosing actions over the edges of a graph solving the full RL problem end to end, and also integrating MCTS with learned prior both at train time and test time (together and independently) (http://proceedings.mlr.press/v97/bapst19a/bapst19a.pdf)
Link: OpenReview
Again a rejected paper due to lack of innovation and SOTA result.
In OpenReview#4 wrote:
Comment: Seems not good enough to use