Overview

The ROS 2 Experimental Reinfocement Learning (RL) package is a package that enables to train and control a robot with RL techniques. This package is utilizing the Gazebo simulator and ROS 2 to train a TurtleBot 3 robot.

Contributing

We have currently implemented a Random Crawl task to get started with RL and plan to implement and add more RL related tasks to this package. This Random Crawl RL task can be used as a tutorial to get started with RL.

Building the source

For instructions on how to build this repo, see the BUILD.md file.

Random Crawl

Random Crawl is a task which enables TurtleBot3 to randomly crawl around the environment without hitting any obstacles. The following use-cases can be achieved with Random Crawl task: 1. Map Exploration 2. Localization test 3. Security Monitoring

Model

TODO Inputs Outputs DQN Parameters

Training

To train Random Crawl:

Firtst run Gazebo:

export <directory_for_turtlebot3_ws>/src/turtlebot3/turtlebot3_simulations/turtlebot3_gazebo/models
export TURTLEBOT3_MODEL=burger or waffle
ros2 launch turtlebot3_gazebo turtlebot3_world.launch.py

Then,

cd <navigation2_ws> source install setup.bash

Run:

ros2 run nav2_turtlebot3_rl random_crawl_train

Running

To run a trained model Random Crawl task: Firtst run Gazebo:

export <directory_for_turtlebot3_ws>/src/turtlebot3/turtlebot3_simulations/turtlebot3_gazebo/models
export TURTLEBOT3_MODEL=burger or waffle
ros2 launch turtlebot3_gazebo turtlebot3_world.launch.py

Then,

cd <navigation2_ws> source install setup.bash

Run:

ros2 run nav2_turtlebot3_rl random_crawl