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This repository includes code for autonomous exploration under uncertainty via Deep Reinforcement Learning (DRL) on Graphs by using a graph neural network (GNN) to represent the state consisting of robot poses, landmarks, and frontiers. Using deep RL significantly reduces computation as the policy can estimate the optimal view instead of explicitly computing it. We use the code of original paper wich is available at https://github.com/RobustFieldAutonomyLab/DRL_graph_exploration.

Environment

Dependency

• Python 3

• PyTorch

• Pytorch Geometric

   pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cu102.html
   pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.7.0+cu102.html
   pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-1.7.0+cu102.html
   pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.7.0+cu102.html
   pip install torch-geometric

• Gtsam (Georgia Tech Smoothing and Mapping library)

   git clone -b emex --single-branch https://bitbucket.com/jinkunw/gtsam
   cd gtsam
   mkdir build && cd build
   cmake ..
   sudo make install

Note: For GTSAM compatability for Python 3 Refer to the following commits:https://bitbucket.org/gtborg/gtsam/commits/0e23f77212477a9146f8844d5e4ddf8d49c346d8

• Pybind11(pybind11 — Seamless operability between C++11 and Python)

  git clone https://github.com/pybind/pybind11.git
  cd pybind11
  mkdir build && cd build
  cmake ..
  sudo make install

• flann.hpp

  sudo apt-get install -y libflann-dev

Issues

The code was compiled on a local machine running Ubuntu 20.04 as the operating system with a GTX 3090 GPU with Cuda 11.2 to speed up training/inference with pytorch. However, because of very large amount of training (1 million epochs as shown in figure 3b in the paper), training was not possible as it took about 2 hours to run for about 12,000 epochs, which is about 83 hours total without checkpointing implemented in the original code. Due to the difficulties compiling on a cloud computing server, we trained for a limited number of epochs (10,000) on the local computer with a GPU.

Compile

You can use the following commands to download and compile the package.

  git clone https://github.com/Zeinab-E/CP8319_Project
  cd DRL_graph_exploration
  mkdir build && cd build
  cmake ..
  make

How to run?

• To run the saved policy:

 cd DRL_graph_exploration/scripts
 python3 test.py