Welcome to the fascinating world of robotics, where deep reinforcement learning (DRL) empowers robots to navigate complex environments! In this blog post, we will explore how a mobile robot uses the Twin Delayed Deep Deterministic Policy Gradient (TD3) neural network to learn navigation in a simulated environment while dodging obstacles.

What You Will Learn

• Overview of the TD3 neural network
• Setting up the environment with ROS Gazebo
• Cloning the repository and compiling the workspace
• Running the training and testing the model
• Troubleshooting tips

Understanding the TD3 Neural Network

Imagine teaching a child how to ride a bike. Initially, they wobble and may fall a few times, but with practice and guidance, they learn to balance and maneuver effectively. Similarly, in our robot scenario, the TD3 neural network helps the robot learn to navigate through a randomly assigned goal point while avoiding obstacles detected by laser readings. This process involves trial and error, with the robot gradually improving its performance based on feedback from its environment.

Setting Up Your Environment

Before diving into the code, ensure you have the required dependencies installed:

• ROS Noetic
• PyTorch
• Tensorboard

Clone the Repository

To get started, open your terminal and clone the necessary code repository:

$ cd ~
$ git clone https://github.com/reiniscimurs/DRL-robot-navigation

Compiling the Workspace

Next, let’s compile the workspace to ensure everything is set up correctly:

$ cd ~/DRL-robot-navigation/catkin_ws
$ catkin_make_isolated

Setting Up Environment Variables

Now, open a terminal and enter the following commands to set up your ROS environment:

$ export ROS_HOSTNAME=localhost
$ export ROS_MASTER_URI=http://localhost:11311
$ export ROS_PORT_SIM=11311
$ export GAZEBO_RESOURCE_PATH=~/DRL-robot-navigation/catkin_ws/src/multi_robot_scenario/launch
$ source ~/.bashrc
$ cd ~/DRL-robot-navigation/catkin_ws
$ source devel_isolated/setup.bash

Running Training

To initiate the training process, navigate to the TD3 folder and run the training script:

$ cd ~/DRL-robot-navigation/TD3
$ python3 train_velodyne_td3.py

Monitoring Training with TensorBoard

You can visualize the training progress using TensorBoard. In a new terminal, run the following:

$ cd ~/DRL-robot-navigation/TD3
$ tensorboard --logdir runs

Killing the Training Process

If you need to stop the training for any reason, you can do so with this command:

$ killall -9 rosout roslaunch rosmaster gzserver nodelet robot_state_publisher gzclient python python3

Testing the Model

Once training is complete, it’s time to test the robot’s navigation capabilities:

$ cd ~/DRL-robot-navigation/TD3
$ python3 test_velodyne_td3.py

Troubleshooting

If you encounter issues during installation or running the code, consider the following troubleshooting tips:

• Ensure all dependencies are correctly installed and compatible with your system.
• Check for any typos in the commands you input.
• Run the terminal commands separately and observe any error messages for specific hints on the underlying issue.

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Conclusion

By following these steps, you will successfully implement a mobile robot that learns to navigate using deep reinforcement learning through the TD3 algorithm. This exciting intersection of AI and robotics showcases the potential for intelligent agents to adapt and improve in their environments.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.