Neural Combinatorial Optimization with Reinforcement Learning
This paper presents a framework to tackle combinatorial optimization problems using neural networks and reinforcement learning. We focus on the traveling salesman problem (TSP) and train a recurrent neural network that, given a set of city coordinates, predicts a distribution over different city permutations. Using negative tour length as the reward signal, we optimize the parameters of the recurrent neural network using a policy gradient method. We compare learning the network parameters on a set of training graphs against learning them on individual test graphs. Without much engineering and heuristic designing, Neural Combinatorial Optimization achieves close to optimal results on 2D Euclidean graphs with up to 100 nodes. Applied to the KnapSack, another NP-hard problem, the same method obtains optimal solutions for instances with up to 200 items. These results, albeit still far from state-of-the-art, give insights into how neural networks can be used as a general tool for tackling combinatorial optimization problems.
This paper proposes to use RNN and reinforcement learning for solving combinatorial optimization problems. The use of pointer network is interesting as it enables generalization to arbitrary input size. The proposed method also *fintunes* on test examples with active search to achieve better performance. The proposed method is theoretically interesting as it shows that RNN and RL can be combined to solve combinatorial optimization problems and achieve comparable performance to traditional heuristic based algorithms. However, the lack of complexity comparison against baselines make it impossible to tell whether the proposed method has any practical value. The matter is further complicated by the fact that the proposed method runs on GPU while baselines run on CPU: it is hard to even come up with a meaningful unit of complexity. Money spent on hardware and electricity per instance may be a viable option. Further more, the performance comparisons should be taken with a grain of salt as traditional heuristic based algorithms can often give better performance if allowed more computation, which is not controlled across algorithms.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Neural Combinatorial Optimization with Reinforcement Learning
This paper presents a framework to tackle combinatorial optimization problems using neural networks and reinforcement learning. We focus on the traveling salesman problem (TSP) and train a recurrent neural network that, given a set of city coordinates, predicts a distribution over different city permutations. Using negative tour length as the reward signal, we optimize the parameters of the recurrent neural network using a policy gradient method. We compare learning the network parameters on a set of training graphs against learning them on individual test graphs. Without much engineering and heuristic designing, Neural Combinatorial Optimization achieves close to optimal results on 2D Euclidean graphs with up to 100 nodes. Applied to the KnapSack, another NP-hard problem, the same method obtains optimal solutions for instances with up to 200 items. These results, albeit still far from state-of-the-art, give insights into how neural networks can be used as a general tool for tackling combinatorial optimization problems.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper proposes to use RNN and reinforcement learning for solving combinatorial optimization problems. The use of pointer network is interesting as it enables generalization to arbitrary input size. The proposed method also *fintunes* on test examples with active search to achieve better performance. The proposed method is theoretically interesting as it shows that RNN and RL can be combined to solve combinatorial optimization problems and achieve comparable performance to traditional heuristic based algorithms. However, the lack of complexity comparison against baselines make it impossible to tell whether the proposed method has any practical value. The matter is further complicated by the fact that the proposed method runs on GPU while baselines run on CPU: it is hard to even come up with a meaningful unit of complexity. Money spent on hardware and electricity per instance may be a viable option. Further more, the performance comparisons should be taken with a grain of salt as traditional heuristic based algorithms can often give better performance if allowed more computation, which is not controlled across algorithms.
assistant
This paper proposes to use RNN and reinforcement learning for solving combinatorial optimization problems. The use of pointer network is interesting as it enables generalization to arbitrary input size. The proposed method also *fintunes* on test examples with active search to achieve better performance. The proposed method is theoretically interesting as it shows that RNN and RL can be combined to solve combinatorial optimization problems and achieve comparable performance to traditional heuristic based algorithms. However, the lack of complexity comparison against baselines make it impossible to tell whether the proposed method has any practical value. The matter is further complicated by the fact that the proposed method runs on GPU while baselines run on CPU: it is hard to even come up with a meaningful unit of complexity. Money spent on hardware and electricity per instance may be a viable option. Further more, the performance comparisons should be taken with a grain of salt as traditional heuristic based algorithms can often give better performance if allowed more computation, which is not controlled across algorithms.