DRL-robot-navigation and DRL-Robot-Navigation-ROS2
About DRL-robot-navigation
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.
About DRL-Robot-Navigation-ROS2
reiniscimurs/DRL-Robot-Navigation-ROS2
Deep Reinforcement Learning for mobile robot navigation in ROS2 Gazebo simulator. Using DRL (SAC, TD3) neural networks, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.
This project helps robotics engineers and researchers train mobile robots to navigate safely and efficiently in simulated environments. It takes sensor data (like laser readings for obstacles and goal coordinates) as input and outputs a trained deep reinforcement learning model. This model enables a robot to reach a specified goal while autonomously avoiding collisions with obstacles.
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