This is implementation of http://web.stanford.edu/~yukez/papers/icra2017.pdf in PyTorch. It attempts to achieve the same results as the Tensorflow implementation, which can be found here: https://github.com/zfw1226/icra2017-visual-navigation.
This repocitory provides a Tensorflow implementation of the deep siamese actor-critic model for indoor scene navigation introduced in the following paper:
Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning
Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph J. Lim, Abhinav Gupta, Li Fei-Fei, and Ali Farhadi
ICRA 2017, Singapore
This code is implemented in Pytorch 0.4. It uses Docker to automate instalation process. In order to run this code, I recommend pulling it from my dockerhub repository.
In order to start training, run those commands:
git clone https://github.com/jkulhanek/visual-navigation-agent-pytorch
docker-compose run train
To facilitate training, we provide hdf5 dumps of the simulated scenes. Each dump contains the agent's first-person observations sampled from a discrete grid in four cardinal directions. To be more specific, each dump stores the following information row by row:
graph[i][j]
is the location id of the destination by taking action j
in location i
, and -1
indicates collision while the agent stays in the same place.-1
means two states are unreachable from each other.
I would like to acknowledge the following references that have offered great help for me to implement the model.
Please cite our ICRA'17 paper if you find this code useful for your research.
@InProceedings{zhu2017icra,
title = {{Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning}},
author = {Yuke Zhu and Roozbeh Mottaghi and Eric Kolve and Joseph J. Lim and Abhinav Gupta and Li Fei-Fei and Ali Farhadi},
booktitle = {{IEEE International Conference on Robotics and Automation}},
year = 2017,
}
MIT