Awesome

QMDP-net

Implementation of the NIPS 2017 paper:

QMDP-Net: Deep Learning for Planning under Partial Observability
Peter Karkus, David Hsu, Wee Sun Lee
National University of Singapore
https://arxiv.org/abs/1703.06692

The code implements the 2D grid navigation domain, and a QMDP-net with 2D state space in tensorflow.

Requirements

Python 2.7
Tensorflow 1.3.0
Python packages: numpy, scipy, tables, pymdptoolbox, tensorpack

To install these packages using pip:

pip install tensorflow
pip install numpy scipy tables pymdptoolbox tensorpack==0.9.1

Optional: to speed up data generation download and install the latest version of pymdptoolbox

git clone https://github.com/sawcordwell/pymdptoolbox.git pymdptoolbox
cd ./pymdptoolbox
python setup.py install

Train and evaluate a QMDP-net

The folder ./data/grid10 contains training and test data for the deterministic 10x10 grid navigation domain (10,000 environments, 5 trajectories each for training, 500 environments, 1 trajectory each for testing).

Train network using only the first 4 steps of each training trajectory:

python train.py ./data/grid10/ --logpath ./data/grid10/output-lim4/ --lim_traj_len 4

The learned model will be saved to ./data/grid10/output-lim4/final.chk

Load the previously saved model and train further using the full trajectories:

python train.py ./data/grid10/ --logpath ./data/grid10/output-lim100/ --loadmodel ./data/grid10/output-lim4/final.chk --lim_traj_len 100

For help on arguments execute:

python train.py --help

Evaluate a previously trained model

A model trained by the commands above is readily available in the folder: data/grid10/trained-model. You may load and evaluate this model using the following command:

python train.py ./data/grid10/ --loadmodel ./data/grid10/trained-model/final.chk --epochs 0

The expected output:

Evaluating 100 samples, repeating simulation 1 time(s)
Expert
Success rate: 1.000  Trajectory length: 7.3  Collision rate: 0.000
QMDP-Net
Success rate: 0.990  Trajectory length: 7.1  Collision rate: 0.000

Generate training and test data

You may generate data using the script grid.py.
As an example, the command for the 18x18 deterministic grid navigation domain is:

python grid.py ./data/grid18/ 10000 500 --N 18 --train_trajs 5 --test_trajs 1

This will generate 10,000 random environments for training, 500 for testing, 5 and 1 trajectories per environment.

For the stochastic variant use:

python grid.py ./data/grid18/ 10000 500 --N 18 --train_trajs 5 --test_trajs 1 --Pmove_succ 0.8 --Pobs_succ 0.9

For help on arguments execute:

python grid.py --help