动态避障 ROS机器人 强化学习

# Reinforcement Learning with DDPG For this project my goal is to create a Deep reinforcement learning Code that can avoid obstacles while trying to get to a target ## Base Idea I saw a code of a DQN agent in the repository of ROBOTIS. But to create a better agent to control Robots I didn't see DQN with too much help in project because it only permits discrete actions. So my idea were to create a DDPG algorithm's agent based. A DDPG agent permits continous control for a robot. In my case I have as outputs: linear velocity (0 ~ 0.22m/s) and angular velocity (-1 ~ 1rad/s). - https://github.com/ROBOTIS-GIT/turtlebot3_machine_learning ## Libraries [Pytorch] ## ROS You can find the packages the I used here: - https://github.com/ROBOTIS-GIT/turtlebot3 - https://github.com/ROBOTIS-GIT/turtlebot3_msgs - https://github.com/ROBOTIS-GIT/turtlebot3_simulations ``` cd ~/catkin_ws/src/ git clone {link_git} cd ~/catkin_ws && catkin_make ``` To install my package you will do the same from above. ## Set State In: turtlebot3/turtlebot3_description/urdf/turtlebot3_burger.gazebo.xacro. ``` <xacro:arg name="laser_visual" default="false"/> # Visualization of LDS. If you want to see LDS, set to `true` ``` And ``` <scan> <horizontal> <samples>360</samples> # The number of sample. Modify it to 10 <resolution>1</resolution> <min_angle>0.0</min_angle> <max_angle>6.28319</max_angle> </horizontal> </scan> ``` ## Run Code I have four stage as in the examples of Robotis. But I dont know yet my code dont have a geat performance in stage 3. First to run: ``` roslaunch turtlebot3_gazebo turtlebot3_stage_{number_of_stage}.launch ``` In another terminal run: ``` roslaunch project ddpg_stage_{number_of_stage}.launch ```