Installation

To do this tutorial, you need to install Tensorflow, Keras and Anaconda with Ubuntu 16.04 and ROS kinetic. http://wiki.ros.org/kinetic/Installation/Ubuntu

Anaconda

You can download Anaconda 5.2[https://www.anaconda.com/download/#linux] for Python 2.7 version.

After downloading Andaconda, go to the directory in located download file and enter the follow command.

 bash Anaconda2-x.x.x-Linux-x86_64.sh

After installing Anaconda,

 source ~/.bashrc

 python -V

If Anaconda is installed, you can see Python 2.7.xx :: Anaconda, Inc..

ROS dependency packages

To use ROS and Anaconda together, you must additionally install ROS dependency packages.

 pip install -U rosinstall msgpack empy defusedxml netifaces

Tensorflow (https://www.tensorflow.org/install/install_linux#the_url_of_the_tensorflow_python_package)

You can install TensorFlow.

 conda create -n tensorflow pip python=2.7

This tutorial is used python 2.7(CPU only). If you want to use another python version and GPU, please refer to TensorFlow.

Activate virtual environment

 source activate tensorflow

conda(cpu)

 pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.8.0-cp27-none-linux_x86_64.whl

conda(gpu), only cuda9.0 supported

 pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.10.1-cp27-none-linux_x86_64.whl

Keras

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow.

conda

 pip install keras

Pytorch (https://pytorch.org/)

 conda create -n pytorch python=2.7 or 3.5

 source activate pytorch

  pip install torch torchvision

 or

 pip3 install torch torchvision

 source /opt/ros/kinetic/setup.bash

I didn't check exactly, it is neccesary components [5][6][7][8].

sudo apt-get install python-dev
git clone https://github.com/lakehanne/soft-neuro-adapt
cd soft-neuro-adapt
pip3 install -r requirements.txt 
pip3 install pyros_setup
pip3 install netifaces rospkg

Install requirements and clone turtlebot packages

 sudo apt-get install ros-kinetic-joy ros-kinetic-teleop-twist-joy ros-kinetic-teleop-twist-keyboard ros-kinetic-laser-proc ros-kinetic-rgbd-launch ros-kinetic-depthimage-to-laserscan ros-kinetic-rosserial-arduino ros-kinetic-rosserial-python ros-kinetic-rosserial-server ros-kinetic-rosserial-client ros-kinetic-rosserial-msgs ros-kinetic-amcl ros-kinetic-map-server ros-kinetic-move-base ros-kinetic-urdf ros-kinetic-xacro ros-kinetic-compressed-image-transport ros-kinetic-rqt-image-view ros-kinetic-gmapping ros-kinetic-navigation ros-kinetic-interactive-markers

 cd ~/catkin_ws/src/

 git clone https://github.com/ROBOTIS-GIT/turtlebot3_msgs.git

 git clone https://github.com/ROBOTIS-GIT/turtlebot3.git

 git clone https://github.com/ROBOTIS-GIT/turtlebot3_simulations.git

Machine Learning packages

WARNING: Please install turtlebot3, turtlebot3_msgs and turtlebot3_simulations package before installing this package.

 cd ~/catkin_ws/src/

 git clone https://github.com/ROBOTIS-GIT/turtlebot3_machine_learning.git

 cd ~/catkin_ws && catkin_make

Set parameters

The goal of DQN Agent is to get the TurtleBot3 to the goal avoiding obstacles. When TurtleBot3 gets closer to the goal, it gets a positive reward, and when it gets farther it gets a negative reward. The episode ends when the TurtleBot3 crashes on an obstacle or after a certain period of time. During the episode, TurtleBot3 gets a big positive reward when it gets to the goal, and TurtleBot3 gets a big negative reward when it crashes on an obstacle.

Set state

State is an observation of environment and describes the current situation. Here, state_size is 26 and has 24 LDS values, distance to goal, and angle to goal.

Turtlebot3’s LDS default is set to 360. You can modify sample of LDS at turtlebot3/turtlebot3_description/urdf/turtlebot3_burger.gazebo.xacro.

<xacro:arg name="laser_visual" default="false"/>   # Visualization of LDS. If you want to see LDS, set to `true`
<scan>
  <horizontal>
    <samples>360</samples>            # The number of sample. Modify it to 24
    <resolution>1</resolution>
    <min_angle>0.0</min_angle>
    <max_angle>6.28319</max_angle>
  </horizontal>
</scan>

Set action

Action is what an agent can do in each state. Here, turtlebot3 has always 0.15 m/s of linear velocity. angular velocity is determined by action.

Action	Angular velocity(rad/s)
0	-1.5
1	-0.75
2	0
3	0.75
4	1.5

Set reward

When turtlebot3 takes an action in a state, it receives a reward. The reward design is very important for learning. A reward can be positive or negative. When turtlebot3 gets to the goal, it gets big positive reward. When turtlebot3 collides with an obstacle, it gets big negative reward. If you want to apply your reward design, modify setReward function at /turtlebot3_machine_learning/turtlebot3_dqn/src/turtlebot3_dqn/environment_stage_#.py.

Set hyper parameters

This tutorial has been learned using DQN. DQN is a reinforcement learning method that selects a deep neural network by approximating the action-value function(Q-value). Agent has follow hyper parameters at /turtlebot3_machine_learning/turtlebot3_dqn/nodes/turtlebot3_dqn_stage_#.

Hyper parameter	default	description
episode_step	6000	The time step of one episode.
target_update	2000	Update rate of target network.
discount_factor	0.99	Represents how much future events lose their value according to how far away.
learning_rate	0.00025	Learning speed. If the value is too large, learning does not work well, and if it is too small, learning time is long.
epsilon	1.0	The probability of choosing a random action.
epsilon_decay	0.99	Reduction rate of epsilon. When one episode ends, the epsilon reduce.
epsilon_min	0.05	The minimum of epsilon.
batch_size	64	Size of a group of training samples.
train_start	64	Start training if the replay memory size is greater than 64.
memory	1000000	The size of replay memory.

Run Machine Learning(Tensorflow)

 export TURTLEBOT3_MODEL=burger

  roslaunch turtlebot3_gazebo turtlebot3_stage_1.launch

conda

  roslaunch turtlebot3_dqn result_graph.launch

conda

 roslaunch turtlebot3_dqn turtlebot3_dqn_stage_1.launch

Building PyQT5 with Python 2.7 on Ubuntu 16.04 (http://www.powerbrowsing.com/2017/12/building-pyqt5-with-python-2-7-on-ubuntu-16-04/)

So you want Qt5, but you have a lot of strict dependencies holding you back from going to Python 3. What to do? Go build everything yourself!

The following are steps based on a fresh install of Ubuntu 16.04, so adapt it per your needs.

First install the latest version of Qt. I was using the free open source edition. At the time of this post, the following was the set of commands given by Qt’s official website. I just downloaded the installation into my Downloads folder and ran it:

First download the installer:

  cd ~/Downloads

  wget https://download.qt.io/official_releases/qt/5.9/5.9.2/qt-opensource-linux-x64-5.9.2.run

Next, adjust the permissions and install Qt:

  chmod +x qt-opensource-linux-x64-5.9.2.run

 ./qt-opensource-linux-x64-5.9.2.run

When you install the Qt-qreator, you select the checkbox about gcc module. In my case, I choiced the Desktop gcc 64-bit checkbox under Qt 5.9.2.

Now, that should have installed Qt5.x on your Ubuntu installation, it is time to install PyQt5 such that it uses Qt5 with Python 2.7. The key in this setup procedure is to build PyQt5 yourself.

First, SIP must be installed before proceeding with building PyQt5. You can download SIP from here. You can also just wget it!

Download here: https://sourceforge.net/projects/pyqt/files/sip/sip-4.19.6/sip-4.19.6.tar.gz

Now is where things get important, as I always prefer to use virtual environments when working with Python, rather than installing everything on the global Python installation. After extracting the file, make sure to run configure.py using your virtual environment’s Python!

 tar xvf sip-4.19.6.tar.gz

 cd sip-4.19.6/

Build configure.py using python command in virtualenv

 ~/path/to/virtualenv/python configure.py

Example)

(conda) python configure.py

If the configuration was successful, the output should tell you that things related to SIP will be installed in your virtual environment’s folder, and NOT in your global Python that resides in /usr.

Now, running make and sudo make install will install SIP into your virtual environment folder, even if you are using sudo.

 make

 sudo make install

Now it is time to download PyQt5 and build it!

 cd ~/Download

 wget https://sourceforge.net/projects/pyqt/files/PyQt5/PyQt-5.9.2/PyQt5_gpl-5.9.2.tar.gz

 tar xvf PyQt5_gpl-5.9.2.tar.gz

Configuration of PyQt5 is needed for building it. Again, using the right interpreter and the right parameters are key.

Build configure.py using python command in virtualenv

 ~/my_virtual_env_path/to/python configure.py -d ~/my_virtual_env_path/lib/python2.7/site-packages/ --sip=/my_virtual_env_path/bin/sip --sip-incdir=../sip-4.19.3/siplib/ --qmake ~/Qt5.9.0/5.9.0/gcc_64/bin/qmake