reiniscimurs/DRL-robot-navigation
Deep Reinforcement Learning for mobile robot navigation in ROS 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.
This project enables a simulated mobile robot to learn how to navigate to a target destination while avoiding obstacles. It takes laser sensor readings and a goal in polar coordinates as input, and outputs trained navigation policies. This is useful for robotics researchers and engineers who are developing autonomous navigation systems.
1,262 stars.
Use this if you are developing or researching autonomous robot navigation and need a framework for training deep reinforcement learning models in a simulated environment like ROS Gazebo.
Not ideal if you need a plug-and-play solution for real-world robot deployment without prior robotics development experience.
Stars
1,262
Forks
189
Language
Python
License
MIT
Category
Last pushed
Dec 13, 2025
Commits (30d)
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