https://github.com/kanakkabara/Autonomous-Drifting

Autonomous Drifting using Reinforcement Learning
https://github.com/kanakkabara/Autonomous-Drifting

deep-reinforcement-learning dqn gazebo matlab openai-gym reinforcement-learning ros

Last synced: 3 months ago
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Autonomous Drifting using Reinforcement Learning

• Host: GitHub
• URL: https://github.com/kanakkabara/Autonomous-Drifting
• Owner: kanakkabara
• Created: 2017-12-13T11:51:48.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2023-03-24T22:24:44.000Z (over 1 year ago)
• Last Synced: 2024-05-02T18:08:17.008Z (6 months ago)
• Topics: deep-reinforcement-learning, dqn, gazebo, matlab, openai-gym, reinforcement-learning, ros
• Language: Jupyter Notebook
• Homepage: http://i.cs.hku.hk/fyp/2017/fyp17014/
• Size: 538 MB
• Stars: 92
• Watchers: 9
• Forks: 22
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
# Autonomous-Drifting

Autonomous Drifting using Reinforcement Learning 

## Installation 

1. sudo ./setup_env.sh

2. cd fyp_ws

3. catkin_make

3. . devel/setup.bash (add ``` source [full path to setup.bash] ``` in your .bashrc)

4. roscd drift_car_env/scripts/

5. sudo pip install -e .

6. roscd drift_car/scripts/rl

7. sudo pip install -r requirements.txt

The first time you open Gazebo, it will download all models from the Gazebo servers, which may take some time. Run ``` rosrun gazebo_ros gazebo ``` to run Gazebo and install models.

## Commands

To run | Command

--- | --- 

ROS Core | ``` roscore ```

Gazebo Simulator | ``` roslaunch drift_car_gazebo drift_car.launch ```

Controller | ``` roslaunch drift_car_gazebo_control drift_car_control.launch ```

Keyboard Teleop | ``` rosrun drift_car_gazebo_control teleop_gazebo.py ```

Joystick Gazebo Controller | ``` rosrun drift_car_gazebo_control joystick_gazebo.py ```

Joystick Car Controller | ``` rosrun drift_car_gazebo_control joystick_car.py ```

Double Dueling Deep Q-Network | ```rosrun drift_car main.py```

## PILCO

1. Install MATLAB, enabled with Robotics System Toolbox. 

2. Add src/drift_car/scripts/rl/modules and src/drift_car/scripts/rl/pilco to MATLAB path.

3. Start the bridge library with ```rosrun drift_car_env matlab_bridge.py```.

4. To train - ```drift_car_learn```. 

5. To apply learned controller - ```applyController```.

## Car Model

To run using the Monster Truck, ```rosed drift_car_gazebo drift_car.launch``` and toggle the comments to load ```truck.xacro.urdf```.