Improving Multi-agent Coordination by Learning to Estimate Contention
Authors: Panayiotis Danassis, Florian Wiedemair, Boi Faltings
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide a thorough evaluation in a variety of synthetic benchmarks and a real-world meeting scheduling problem. In all of them ALMA-Learning is able to quickly (as little as 64 training steps) reach allocations of high social welfare (less than 5% loss) and fairness (up to almost 10% lower inequality compared to the best performing baseline). |
| Researcher Affiliation | Academia | Panayiotis Danassis , Florian Wiedemair and Boi Faltings Artificial Intelligence Laboratory, Ecole Polytechnique F ed erale de Lausanne (EPFL), Switzerland {panayiotis.danassis, florian.wiedemair, boi.faltings}@epfl.ch |
| Pseudocode | Yes | The pseudo-codes for ALMA and ALMA-Learning are presented in Algorithms 1 and 2, respectively. |
| Open Source Code | No | The paper refers to supplementary material in [Danassis et al., 2021] for implementation details, but does not provide a direct link to open-source code for the methodology within the paper itself or the supplementary material reference. The arXiv link is to the paper itself, not the code. |
| Open Datasets | Yes | We evaluate ALMA-Learning in a variety of synthetic benchmarks and a meeting scheduling problem based on real data from [Romano and Nunamaker, 2001]. |
| Dataset Splits | No | The paper mentions 'training time-steps' and 'evaluation time-steps' related to the learning process, but does not provide specific train/validation/test dataset splits for reproducibility. |
| Hardware Specification | No | The paper mentions 'on-device deployment' for potential applications but does not specify the hardware (e.g., CPU, GPU models) used to conduct its experiments. |
| Software Dependencies | No | The paper mentions 'IBM ILOG CP optimizer' as a baseline solver but does not provide specific version numbers for it or any other software dependencies used in their own implementation. |
| Experiment Setup | Yes | ALMA-Learning was trained for 512 time-steps. The loss of rstart is then updated according to the following averaging process, where α is the learning rate: loss[rstart] (1 α)loss[rstart] + α (u(rstart) u(rwon)) |