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.
Train a robot to navigate and avoid obstacles in a simulated environment using deep learning techniques.
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Free · no card · unsubscribe anytimeDeep 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.
DRL-robot-navigation has 1.3k stars on GitHub. It has been forked 194 times. DRL-robot-navigation is written mainly in Python. It has been in active development since 2021. DRL-robot-navigation is available under the MIT license. Its main topics are deep-learning, deep-reinforcement-learning, gazebo, obstacle-avoidance.
What is 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.
Is DRL-robot-navigation open source?
DRL-robot-navigation is an open-source project. It is released under the MIT license.
Is DRL-robot-navigation free?
Yes. DRL-robot-navigation is free and open source — you can use, modify and self-host it.
What license does DRL-robot-navigation use?
DRL-robot-navigation is available under the MIT license.
What language is DRL-robot-navigation written in?
DRL-robot-navigation is written mainly in Python.
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