Gazebo Reinforcement Learning Environment

This repo contains a few reinforcement learning environements and example scripts greatly inspired by openai_ros. The environments are build with ROS ecosystem, Gazebo-7.0 simulation software and OpenAI-gym toolkit.

Pre-requisite

Install ROS-Kinetic, recommand Desktop-Full install.
(Optional but recommend) Install catkin-command-line-tools
(Optional, skip this step if you've installed ROS in Desktop-Full mode) Install Gazebo-7.0
Install OpenAI-gym

pip install gym
(Optional) Install openai_ros
Setup turtlebot Gazebo simulation environment
- Assume you have set up an ros workspace at /home/yourname/ros_ws, and ready to config the environment in it. $ sudo apt install ros-kinetic-turtlebot-gazebo
- To make sure turtlebot_gazebo is launchable, try $ roslaunch turtlbot_gazebo turtlebot_world.launch
To get ready for reinforcement learning, $ cd ~/ros_ws/src, then clone this repo $ git clone https://github.com/deePurrobotics/gazebo_rl.git
$ cd ~/ros_ws, then $ catkin build

Reinforcement Learning Environments

All codes are located in *this_repo*/scripts/ Copy the model files of cable-driven joint into local gazebo models library $ cp -a *this_repo*/gazebo_models/* ~/.gazebo/models/

CribNav-v0

Launch learning environment, $ roslaunch gazebo_rl turtlebot_crib.launch
Open a new terminal, you can $ rosrun gazebo_rl crib_nav_qtable to start a q-learning"

make sure to $ chmod +x *this_repo*/scripts/turtlebot/crib_nav_qtable.py

PlaygPlaygroundFetch-v0

Launch simulation, $ roslaunch gazebo_rl turtlebot_playground.launch
To test this env, $ rosrun gazebo_rl pground_env_test

CablePoint-v0

CMake force plugin for the cable-driven joint

$ cd *this_repo*/worlds/cable_world/
$ mkdir build
$ cd build
$ cmake ..
$ make