Goal-Driven Deep RL Policy for Robot Navigation

Deep Reinforcement Learning for mobile robot navigation in ROS2 Gazebo simulator. Using Twin Delayed Deep Deterministic Policy Gradient (TD3) neural network, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles. Trained in ROS2 Humble & Gazebo simulator with PyTorch.

How To Run

Install Python 3.10, ROS2 Humble, Gazebo 11 on Ubuntu 22.04

git clone git@github.com:vishweshvhavle/deep-rl-navigation.git
cd deep-rl-navigation/DRL_robot_navigation_ros2/
sudo rosdep init
rosdep install --from-paths src --ignore-src -y
colcon build
cd ..

For Training -

ros2 launch td3 train_simulation.launch.py

For Testing -

ros2 launch td3 test_simulation.launch.py

Academic Integrity

If you are currently enrolled in this course, please refer to IIIT-Delhi's Policy on Academic Integrity before referring to any of the repository contents. This repository contains the work we did as undergrads at IIIT-Delhi in CSE-564 Reinforcement Learning course. We do not encourage plagiarism of any kind.