Neural Regret-Matching for Distributed Constraint Optimization Problems
Authors: Yanchen Deng, Runsheng Yu, Xinrun Wang, Bo An
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive empirical evaluations indicate that our algorithm can scale up to large-scale DCOPs and significantly outperform the state-of-the-art methods. |
| Researcher Affiliation | Academia | School of Computer Science and Engineering, Nanyang Technological University, Singapore |
| Pseudocode | Yes | Technical proofs, pseudo codes and additional results are provided in the appendix, which can be found at https://personal.ntu.e du.sg/boan/papers/IJCAI21 Deep DCOP Appendix.pdf. |
| Open Source Code | No | Technical proofs, pseudo codes and additional results are provided in the appendix, which can be found at https://personal.ntu.e du.sg/boan/papers/IJCAI21 Deep DCOP Appendix.pdf. This link provides supplementary material in PDF format, not the open-source code for the methodology described. |
| Open Datasets | No | The paper describes how problem instances for 'random DCOPs', 'scale-free network problems', and 'sensor network problems' were generated (e.g., 'randomly establish a constraint', 'use Barab asi-Albert model to generate', 'costs are uniformly selected from [0,100]'), but it does not provide concrete access information (links, DOIs, or specific citations for pre-existing public datasets) for the data used in experiments. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | Yes | All experiments are conducted on an i7 octa-core workstation with 32 GB memory. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers (e.g., Python 3.8, PyTorch 1.9, TensorFlow 2.x). |
| Experiment Setup | Yes | We set δt = t0.45 and consider each estimator as a neural network with two hidden layers. Each hidden layer has 16 neurons and uses relu as the activation function. Each time the neural networks are trained by 2 steps of mini-batch stochastic gradient descent (SGD) with a batch size of 32. We use Adam optimizer [Kingma and Ba, 2014] with a learning rate of 2 10 3 to update parameters. Finally, we set difference budget b = 4, γ = 0.9 and the capacity of the memory to 5000 regret values. |