PyTorch implementation of the Value Iteration Networks (VIN) (NIPS '16 best paper)

Last update: Nov 24, 2022

Overview

Value Iteration Networks in PyTorch

Tamar, A., Wu, Y., Thomas, G., Levine, S., and Abbeel, P. Value Iteration Networks. Neural Information Processing Systems (NIPS) 2016

This repository contains an implementation of Value Iteration Networks (VIN) in PyTorch based on the original Theano implementation by the authors and the TensoFlow implementation by Abhishek Kumar.

VIN won the Best Paper Award at NIPS 2016.

Dependencies

Python 2.7
PyTorch
SciPy >= 0.18.1 (to load the data)

Datasets

The datasets is from the author's repository. This repository contains the 8x8 GridWorld dataset for convenience and its small size.
utils.py and data.py are from Abhishek Kumar's repository

Training

python train_main.py

Several arguments can be set in train_main.py like learning rate. Please check train_main.py for details.

python train_main.py --lr 0.001

References

Codes accompanying the paper "Learning Nearly Decomposable Value Functions with Communication Minimization" (ICLR 2020)

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PyTorch implementation of the Value Iteration Networks (VIN) (NIPS '16 best paper)

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Overview

Value Iteration Networks in PyTorch

Dependencies

Datasets

Training

References

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