Multi-Agent Routing Value Iteration Network
Authors: Quinlan Sykora, Mengye Ren, Raquel Urtasun
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We created a simulated environment to mimic realistic mapping performed by autonomous vehicles with unknown minimum edge coverage and traffic conditions; our approach significantly outperforms traditional solvers both in terms of total cost and runtime. We compare our approach with the following baselines. |
| Researcher Affiliation | Industry | Quinlan Sykora * Mengye Ren * Raquel Urtasun Correspondence to: Quinlan Sykora <quinlan.sykora@uber.com>, Mengye Ren <mren3@uber.com>, Raquel Urtasun <urtasun@uber.com>. |
| Pseudocode | No | The paper describes the algorithm steps in paragraph form, but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Our code and data are released at https://github.com/ uber/MARVIN |
| Open Datasets | Yes | Our code and data are released at https://github.com/ uber/MARVIN and The dataset contains 22,814 directed road graphs collected from 18 cities around the world. |
| Dataset Splits | Yes | We use a separate city for testing purposes and 10% of the training set for validation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running experiments. |
| Software Dependencies | No | The paper mentions using the Adam optimizer, but does not provide specific version numbers for software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | When training, we set the learning rate of our model to be 1e-3 using the Adam optimizer, with a decay rate of 0.1 every 2000 epochs. We train our model for 5000 epochs. We use a batch size of 50 graphs, each of which has up to 25 nodes. |