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VIN: Value Iteration Networks

Architecture of Value Iteration Network

A quick thank you

A few others have released amazing related work which helped inspire and improve my own implementation. It goes without saying that this release would not be nearly as good if it were not for all of the following:

Why another VIN implementation?

  1. The Pytorch VIN model in this repository is, in my opinion, more readable and closer to the original Theano implementation than others I have found (both Tensorflow and Pytorch).
  2. This is not simply an implementation of the VIN model in Pytorch, it is also a full Python implementation of the gridworld environments as used in the original MATLAB implementation.
  3. Provide a more extensible research base for others to build off of without needing to jump through the possible MATLAB paywall.

Installation

This repository requires following packages:

Use pip to install the necessary dependencies:

pip install -U -r requirements.txt

Note that PyTorch cannot be installed directly from PyPI; refer to http://pytorch.org/ for custom installation instructions specific to your needs.

How to train

8x8 gridworld

python train.py --datafile dataset/gridworld_8x8.npz --imsize 8 --lr 0.005 --epochs 30 --k 10 --batch_size 128

16x16 gridworld

python train.py --datafile dataset/gridworld_16x16.npz --imsize 16 --lr 0.002 --epochs 30 --k 20 --batch_size 128

28x28 gridworld

python train.py --datafile dataset/gridworld_28x28.npz --imsize 28 --lr 0.002 --epochs 30 --k 36 --batch_size 128

Flags:

  • datafile: The path to the data files.
  • imsize: The size of input images. One of: [8, 16, 28]
  • lr: Learning rate with RMSProp optimizer. Recommended: [0.01, 0.005, 0.002, 0.001]
  • epochs: Number of epochs to train. Default: 30
  • k: Number of Value Iterations. Recommended: [10 for 8x8, 20 for 16x16, 36 for 28x28]
  • l_i: Number of channels in input layer. Default: 2, i.e. obstacles image and goal image.
  • l_h: Number of channels in first convolutional layer. Default: 150, described in paper.
  • l_q: Number of channels in q layer (~actions) in VI-module. Default: 10, described in paper.
  • batch_size: Batch size. Default: 128

How to test / visualize paths (requires training first)

8x8 gridworld

python test.py --weights trained/vin_8x8.pth --imsize 8 --k 10

16x16 gridworld

python test.py --weights trained/vin_16x16.pth --imsize 16 --k 20

28x28 gridworld

python test.py --weights trained/vin_28x28.pth --imsize 28 --k 36

To visualize the optimal and predicted paths simply pass:

--plot

Flags:

  • weights: Path to trained weights.
  • imsize: The size of input images. One of: [8, 16, 28]
  • plot: If supplied, the optimal and predicted paths will be plotted
  • k: Number of Value Iterations. Recommended: [10 for 8x8, 20 for 16x16, 36 for 28x28]
  • l_i: Number of channels in input layer. Default: 2, i.e. obstacles image and goal image.
  • l_h: Number of channels in first convolutional layer. Default: 150, described in paper.
  • l_q: Number of channels in q layer (~actions) in VI-module. Default: 10, described in paper.

Results

Gridworld Sample One Sample Two
8x8
16x16
28x28

Datasets

Each data sample consists of an obstacle image and a goal image followed by the (x, y) coordinates of current state in the gridworld.

Dataset size 8x8 16x16 28x28
Train set 81337 456309 1529584
Test set 13846 77203 251755

The datasets (8x8, 16x16, and 28x28) included in this repository can be reproduced using the dataset/make_training_data.py script. Note that this script is not optimized and runs rather slowly (also uses a lot of memory :D)

Performance: Success Rate

This is the success rate from rollouts of the learned policy in the environment (taken over 5000 randomly generated domains).

Success Rate 8x8 16x16 28x28
PyTorch 99.69% 96.99% 91.07%

Performance: Test Accuracy

NOTE: This is the accuracy on test set. It is different from the table in the paper, which indicates the success rate from rollouts of the learned policy in the environment.

Test Accuracy 8x8 16x16 28x28
PyTorch 99.83% 94.84% 88.54%