Hopper Robot Q-Learning

This project provides a simulation environment for a Hopper robot in Gazebo using ROS Kinetic, as well as Q-Learning algorithms to train the robot to perform various tasks. The project uses the OpenAI Gym package to create a gym environment for the robot simulation and implements Q-Learning algorithms to train the robot to perform various tasks.

Installation

ROS Installation

1. Follow the instructions for installing ROS Kinetic from the official ROS website based on your operating system: http://wiki.ros.org/kinetic/Installation
2. Once ROS is installed, set up your ROS environment by sourcing the setup.bash file:

source /opt/ros/kinetic/setup.bash

Gazebo Installation

1. Install Gazebo using the following command:

sudo apt-get install gazebo9

2. Install additional dependencies for Gazebo and ROS integration:

sudo apt-get install ros-kinetic-gazebo-ros-pkgs ros-kinetic-gazebo-ros-control

Gym Installation

1. Install the OpenAI Gym package using the following command:

pip install gym

2. Install additional dependencies required for the Hopper Robot Q-Learning project:

pip install numpy

Downloading and Running the Project

1. Clone this repository to your local machine:

git clone https://github.com/shiivashaakeri/Hopper_Robot_QLeaning.git

2. Navigate to the root directory of your ROS workspace:

cd path/to/simulation_ws

3. Build your workspace:

catkin_make

4. Source the setup.bash file for your workspace:

source devel/setup.bash

5. Launch the main launch file using roslaunch:

roslaunch my_hopper_training main.launch

Usage

Once the simulation is running, you can use the OpenAI Gym interface to interact with the simulation and train the robot using Q-Learning algorithms. The start_training_v2.py file contains the implementation of the Q-Learning algorithm and can be modified to train the robot for different tasks.

Acknowledgements

• ROS and Robotics tutorials
• Reinforcement learning environment for developing AI algorithms
• Gazebo Simulator Tutorials with ROS