hi-abhi/tensorflow-value-iteration-networks
TensorFlow implementation of the Value Iteration Networks (NIPS '16) paper
This project helps machine learning researchers and developers explore and implement Value Iteration Networks (VINs). It takes GridWorld datasets (or similar structured spatial data) as input and produces a trained model capable of predicting optimal paths or actions in environments where planning is crucial. It is primarily for those working on advanced AI algorithms for decision-making in complex environments.
552 stars. No commits in the last 6 months.
Use this if you are a machine learning researcher or developer interested in experimenting with or building upon Value Iteration Networks for tasks requiring integrated perception and planning.
Not ideal if you are looking for a plug-and-play solution for a specific navigation or planning problem without needing to understand or modify the underlying deep learning model.
Stars
552
Forks
118
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 07, 2019
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/hi-abhi/tensorflow-value-iteration-networks"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
MaximeVandegar/Papers-in-100-Lines-of-Code
Implementation of papers in 100 lines of code.
kk7nc/RMDL
RMDL: Random Multimodel Deep Learning for Classification
OML-Team/open-metric-learning
Metric learning and retrieval pipelines, models and zoo.
miguelvr/dropblock
Implementation of DropBlock: A regularization method for convolutional networks in PyTorch.
PaddlePaddle/models
Officially maintained, supported by PaddlePaddle, including CV, NLP, Speech, Rec, TS, big models...