This repository contains code for robot exploration with Deep Reinforcement Learning (DRL). The agent utilizes the local structure of the environment to predict robot’s optimal sensing action. A demonstration video can be found here.

• Python 3
• scikit-image

  pip3 install scikit-image
  

• tensorboardX

  pip3 install tensorboardX
  

• TensorFlow (this code is writen under TF1.x but it is modified to be compatible with TF2)
• 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
  

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

git clone https://github.com/RobustFieldAutonomyLab/DRL_robot_exploration.git
cd DRL_robot_exploration
mkdir build && cd build
cmake ..
make

• For the CNN policy:

  cd DRL_robot_exploration/scripts
  python3 tf_policy_cnn.py
  

• For the RNN policy:

  cd DRL_robot_exploration/scripts
  python3 tf_policy_rnn.py
  

• To select the running mode, at the beginning of the tf_policy code:

  # select mode
  TRAIN = False
  PLOT = True
  

  Set TRAIN=False to run the saved policy. You can train your own policy by setting TRAIN=True. Set PLOT=True to show visualization plots.

• To show the average reward during the training:

  cd DRL_robot_exploration
  tensorboard --logdir=log
  

Please cite our paper if you use any of this code:

@inproceedings{ExplorDRL2019,
  title={Self-Learning Exploration and Mapping for Mobile Robots via Deep Reinforcement Learning},
  author={Chen, Fanfei and Bai, Shi and Shan, Tixiao and Englot, Brendan},
  booktitle={AIAA SciTech Forum},
  pages={0396},
  year={2019},
}

• DeepRL-Agents
• DeepLearningFlappyBird
• Random Dungeon Generator
• PyAstar