vishweshvhavle/deep-rl-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.
This project helps robotics engineers and researchers develop and test navigation policies for mobile robots. It takes a simulated robot within a Gazebo environment and trains it to navigate to various goal points while avoiding obstacles. The output is a trained control policy that allows the robot to move autonomously and efficiently.
No commits in the last 6 months.
Use this if you are a robotics developer or researcher looking to train a mobile robot to navigate to a goal in a simulated environment using deep reinforcement learning.
Not ideal if you need to deploy a navigation solution directly onto a physical robot without prior simulation or if you require real-world hardware integration for testing.
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49
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Language
Python
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Last pushed
Dec 13, 2023
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