• Ubuntu 20.04 LTS (Focal Fossa) download
• ROS2 Foxy Fitzroy
• Gazebo (Version 11.0)
• PyTorch (Version: 1.10.0)

Install ROS2 foxy according to the following guide: link. You can choose either the Desktop or Bare Bones ROS installation, both work.
To prevent having to manually source the setup script everytime, add the following line at the end of your ~/.bashrc file:

source /opt/ros/foxy/setup.bash

More detailed installation instructions can be found here.

For this project we will be using Gazebo 11.0. To install Gazebo 11.0, navigate to the following page, select Version 11.0 in the top-right corner and follow the default installation instructions.

Next, we need to install a package which allows ROS2 to interface with Gazebo. To install this package we simply execute the following command in a terminal:

sudo apt install ros-foxy-gazebo-ros-pkgs

After successfull installation we are now going to test our ROS2 + Gazebo setup by making a demo model move in the simulator. First install two additional packages for demo purposes (they might already be installed):

sudo apt install ros-foxy-ros-core ros-foxy-geometry2

Source ROS2 before we launch the demo:

source /opt/ros/foxy/setup.bash

Now, let's load the demo model in gazebo:

gazebo --verbose /opt/ros/foxy/share/gazebo_plugins/worlds/gazebo_ros_diff_drive_demo.world

This should launch the Gazebo GUI with a simple vehicle model. Open a second terminal and provide the following command to make the vehicle move:

ros2 topic pub /demo/cmd_demo geometry_msgs/Twist '{linear: {x: 1.0}}' -1

If the vehicle starts moving forward we confirmed that the Gazebo-ROS connection works. If something does not work, carefully check whether you executed all the commands and sourced ROS2 (source /opt/ros/foxy/setup.bash). You can also check the more detailed guide.

If you are using Ubuntu 20.04 as specified, Python should already be preinstalled. The last tested vesion for this project was Python 3.8.10

Install pip3 (python package manager for python 3) as follows:

sudo apt install python3-pip

To install the tested version of PyTorch (1.10.0) with CUDA support (11.3) and packages for generating graphs, run:

pip3 install matplotlib pandas pyqtgraph==0.12.4 PyQt5==5.14.1 torch==1.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html

pyqtgraph and PyQt are optional and only necessary if you want to visualize the neural network activity. pandas is only required for generating graphs outside of training.

Note: The version of CUDA support to install will depend on the compute capability of your GPU

We can significantly speed up the training procedure by making use of a GPU when available in your computer. If no GPU is available or it is not initalized correctly the training will automatically be redirected to the CPU. Since most users have access to an NVIDIA GPU we will explain how to enable this to work with PyTorch on linux. Three different components are required to train on GPU:

• NVIDIA drivers for linux
• The CUDA library for linux
• cuDNN (comes with pytorch and should be installed automatically)

Press the windows/command key and type "Additional drivers" to make the corresponding linux menu come up. Here, multiple radio button options should be listed for installing different nvidia drivers. Install the option with the lastest version (highest number, e.g. currently nvidia-driver-510).

The next step is to download the correct CUDA version. This will depend on your NVIDIA drivers and GPU variant. Generally, all you have to do is execute:

sudo apt install nvidia-cuda-toolkit

You can then verify that CUDA is installed using:

nvcc -V

and

nvidia-smi

Which should display version numbers and GPU information. In case of doubt, consult one the following resources: one, two, three

Now it's time to download the repository to the actual code.

Since ROS2 does not yet support metapackages, we will have to download the whole workspace from Git.

First, make sure you have the turtlebot3-description package by running:

sudo apt-get install ros-foxy-turtlebot3-description

Open a terminal in the desired location for the new workspace. Clone the repository using:

git clone https://github.com/tomasvr/turtlebot3_drlnav.git

cd into the directory and make sure you are on the main branch

cd turtlebot3_drlnav
git checkout main

Next, install the correct rosdep tool

sudo apt install python3-rosdep2

Then initalize rosdep by running

rosdep update

Now we can use rosdep to install all ROS packaged needed by our repository

rosdep install -i --from-path src --rosdistro foxy -y

Now that we have all of the packages in place it is time to build the repository. First update your package list

sudo apt update

Then install the build tool colcon which we will use to build our ROS2 package

sudo apt install python3-colcon-common-extensions

Next, it's time to actually build the repository code!

colcon build

After colcon has finished building source the repository

source install/setup.bash

The last thing to we need to do before running the code is add a few lines to our ~/.bashrc file so that they are automatically executed whenever we open a new terminal. Add the following lines at the end of your ~/.bashrc file and replace ~/path/to/turtlebot3_drlnav/repo by the path where you cloned the repository. (e.g. ~/turtlebot3_drlnav)

# ROS2 domain id for network communication, machines with the same ID will receive each others' messages
export ROS_DOMAIN_ID=1

# Fill in the path to where you cloned the turtlebot3_drlnav repo
WORKSPACE_DIR=~/path/to/turtlebot3_drlnav
export DRLNAV_BASE_PATH=$WORKSPACE_DIR

# Source the workspace
source $WORKSPACE_DIR/install/setup.bash

# Allow gazebo to find our turtlebot3 models
export GAZEBO_MODEL_PATH=$GAZEBO_MODEL_PATH:$WORKSPACE_DIR/src/turtlebot3_simulations/turtlebot3_gazebo/models

# Select which turtlebot model we will be using (default: burger, waffle, waffle_pi)
export TURTLEBOT3_MODEL=burger

# Allow Gazebo to find the plugin for moving the obstacles
export GAZEBO_PLUGIN_PATH=$GAZEBO_PLUGIN_PATH:$WORKSPACE_DIR/src/turtlebot3_simulations/turtlebot3_gazebo/models/turtlebot3_drl_world/obstacle_plugin/lib

For more detailed instructions on ros workspaces check this guide.

Note: Always make sure to first run source install/setup.bash or open a fresh terminal after building with colcon build.

Now that we have finally completed the setup, all that's left to do is run and train the agent.

Open up four different terminals however you like (I recommended using terminator or tmux for multi-tab). In the first terminal run

ros2 launch turtlebot3_gazebo turtlebot3_drl_stage4.launch.py

You should see the gazebo GUI come up with the robot model loaded and two moving obstacles (this might take a while to load). Note: always run the 'launch' command first before any other nodes as it will set the stage parameter for all of the other the nodes.

In a second terminal run

ros2 run turtlebot3_drl drl_gazebo

In a third terminal run

ros2 run turtlebot3_drl drl_environment

And lastly, in the fourth terminal run the ddpg agent For DDPG:

ros2 run turtlebot3_drl drl_agent ddpg 1

The first argument indicates whether we are testing or training (0 = testing, 1 = training)

for TD3:

ros2 run turtlebot3_drl drl_agent td3 1

for DQN:

ros2 run turtlebot3_drl drl_agent dqn 1

Your robot should now be moving and training progress is being printed to the terminals!

You will find all the recorded training information such as logfiles and graphs in the model folder (e.g. ddpg_0) within the model directory. Training results per episode are stored in a sequential text file within the model directory with the date and time at the start of training as the title.

Now you have everything up and running to train your model. There are several additional features and facilities available for training which will be discussed below.

The current state of the agent (weights, parameters, replay buffer and graphs) will be stored automatically every N episodes as configured by the store_interval variable in the common/settings.py file.

In order to load a model for testing (e.g. ddpg_0 at episode 500) the following command should be used:

ros2 run turtlebot3_drl drl_agent ddpg 0 "ddpg_0" 500

In order to load a model to continue training (e.g. ddpg_0 at episode 500) the following command should be used:

ros2 run turtlebot3_drl drl_agent ddpg 1 "ddpg_0" 500

Note: If you are loading a model on a different stage than it was trained on (e.g. for transfer learning or testing generalizabilty) you have to add a 4th argument specifying the current stage. For example, model ddpg_0 which was trained on stage 4 can be evaluated in stage 3 using the following command

ros2 run turtlebot3_drl drl_agent ddpg 0 "ddpg_0" 500 3

(the original training stage is specified in training logfile (e.g trainstage2_*.txt)

Two examples are included for a DDPG and TD3 model. In order to run them, open four terminals like before and run:

Terminal 1:

ros2 launch turtlebot3_gazebo turtlebot3_drl_stage9.launch.py

Terminal 2:

ros2 run turtlebot3_drl drl_gazebo

Terminal 3:

ros2 run turtlebot3_drl drl_environment

Terminal 4: For DDPG:

ros2 run turtlebot3_drl drl_agent ddpg 0 'examples/ddpg_0' 8000

Or, for TD3

ros2 run turtlebot3_drl drl_agent td3 0 'examples/td3_0' 7400

The pretrained model should then start to navigate successfully.

Note: Do not include 'examples/' in the command when running models trained on your own machine.

You can can switch between 10 different environments by changing the stage number in the launch command:

ros2 launch turtlebot3_gazebo turtlebot3_drl_stage5.launch.py

change stage5 to and stage between 1-10 to train on different environments.

The settings.py file contains most of the interesting parameters that you might wish to change, including the DRL hyperparameters.

The reward.py file contains the reward design. Here you can implement different reward functions between which you can switch in the settings.py file. For example, to define a new reward function "B" you simply add get_reward_B(...) to reward.py and define the reward per step.

To enable the robot to also move in the backward direction simply set ENABLE_BACKWARD to True in settings.py.

In order to compare results the repository includes a script which graphs the reward curves for different models. The script reward_graph.py can be found in the util directory.

To compare the reward curve for ddpg_0 and td3_0 every 100 episodes we type:

python3 util/reward_graph.py 100 examples/ddpg_0 examples/td3_0

Again, examples/ should not be included in the path for your own models.

Note: graph generation will only work if training has not been stopped and continued. Otherwise you first need to merge the different _train_stage.txt files into a single file*

Training models can generate a lot of data, especially if you save the model often. In order to automatically clean models to save space, two cleaning scripts are included in the util folder.

CAUTION: use the following scripts with caution as deleted data cannot be retrieved! Carefully examine the script settings and double check the command line arguments you enter

The following line will clean ddpg_0 removing all saved model states except:

• The model state for the 4 best performing episodes
• The model state for the most recent episode python3 util/clean_single_model.py ddpg_0

If you want to keep more or less data you can adjust the TOP_EPISODES parameters in the script itself.

To clean all of the models at once you can use the purge_all_models.py script. Carefully check the parameters at the top of the file before executing the script. To execute the script simply type: python3 util/purge_all_models.py

The script will loop through all of your models and select the models to keep like explained above. In addition, models which scored too low or were trained for too few episodes will be removed completely (threshold specified in purge_all_models.py).

To enable a complete visualization of the neural network neuron activity and biases simply set ENABLE_VISUAL to True in settings.py. This requires the python3 packages pyqtgraph and PyQt5 to be installed. The visual should mainly be used during evaluation as it can slow down training significantly.

Note: If you get an error message saying that matplotlib is not responding, simply click wait and wait until the episode finishes.

'Frame stacking' can enable the robot to consider the direction in which obstacles are moving. The robot considers multiple subsequent laser scan frames instead of a single frame at each step. To enable frame stacking, set ENABLE_STACKING to True in settings.py. Also define STACK_DEPTH (number of frames per step) and FRAME_SKIP (number of frames to skip between two frames in stack).

drl_agent:

ros2 run turtlebot3_drl drl_agent [algorithm=dqn/ddpg/td3] [mode=0/1] [loadmodel=\path\to\model] [loadepisode=episode] [trainingstage=stage]

algorithm can be either: dqn, ddpg, td3 mode is either: 0 (training) or 1 (evaluating) modelpath is the path to the model to load loadepisode is the episode to load from modelpath trainingstage is the original training stage of modelpath (if different from current stage)

The models trained using this framework were validated on a low-cost physical system. Video demonstrations can be found on my YouTube channel.

For some installations it might also be required to add the following line to your ~/bashrc file:

source ~/ros2_foxy/ros2-linux/setup.bash