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gym-turtlebot

A custom Gymnasium environment to get started with deep reinforcement learning (DRL) using ROS 2 and Gazebo.

License: Apache 2.0 Build Status ROS 2: Jazzy Gazebo: Harmonic Python 3.8+

Overview

gym-turtlebot provides a lightweight, modular environment for training reinforcement learning agents using a TurtleBot4 robot simulated in ROS 2 Jazzy and Gazebo Harmonic. It wraps the simulation in a standard Gymnasium interface to enable fast prototyping and evaluation of DRL algorithms in realistic robotics environment.

Features

  • ROS 2–based robot control integration
  • Customizable Gazebo simulation worlds
  • Gymnasium-compliant API
  • Supports headless and visual simulation
  • Easily extendable for custom robots

Installation

Using Docker

Prerequisites

[!NOTE] The Docker setup is adapted from vscode_ros2_workspace.

Refer to the template repository for more detailed instructions on how to use VS-code and docker for ROS.

Steps

  1. Build 
# Clone the repo into your ROS 2 workspace
mkdir ~/ros2_ws
cd ~/ros2_ws
git clone https://github.com/anurye/gym-turtlebot.git

# Install dependencies
cd ~/ros2_ws
./setup.sh

# Build the workspace and source the setup script
cd ~/ros2_ws
./build.sh
source install/local_setup.bash
  1. Simulation
cd ~/ros2_ws
ros2 launch tb4_gz_sim simulation.launch.py
  1. Deep reinforcement training example

    [!IMPORTANT] Gazebo Sim does not support model reset at the moment. To work around this limitation, we reset both the robot's odometry and its pose by using: 1. The SetPose service from the robot localization package to reinitialize odometry, and 2. The SetEntityPose service from the ros_gz_interfaces package to reposition entities in Gazebo.

python3 src/tb4_drl_navigation/tb4_drl_navigation/examples/sac.py train

Local Setup

Prerequisites

Steps

The steps are the same as above.