Reinforcement Learning | TurtleBot3 Robot | Motion Planning for Robots

Reinforcement Learning is a paradigm of Machine Learning Algorithms, that work on the principle of Learning by Doing. Q Learning is one of the most popular Reinforcement Learning algorithm. The algorithm uses Bellman Update Equations to plan paths given the start and goal positions. The algorithm has been demonstrated on the TurtleBot3 robot in ROS (Robot Operating System) based simulation.
Feel free to leave a comment or message me on Twitter/LinkedIn in case of any questions, doubts, suggestions or improvements.
Twitter: / mahnasakshay
LinkedIn: / sakshaymahna
Links
Simulation Code: github.com/SakshayMahna/Robot...
Algorithm Code: github.com/SakshayMahna/Robot...
Introduction to Reinforcement Learning: • An introduction to Rei...
Reinforcement Learning Basics: towardsdatascience.com/reinfo...
Q Learning: www.freecodecamp.org/news/an-...
ROS: www.ros.org/

[@a.t10]

it is a very useful content. thank you everything.

[@ritzierw]

It really is a good RUclips Channel!!! Thanks for upload this video

[@RoboticswithSakshay]

Thank You! Happy to know you like the content.

[@abrahamyalley9973]

how do i combine this with a global path planning algorithm like a star for obstacle avoidance

[@Dancedfsk8]

Great video.

[@kamaravichow]

Awesome

[@RoboticswithSakshay]

Thank You!😊

[@SPeeDKiLL45]

Please can you recommend me a website where i can view some projects regarding motion planning ? Something i can implement and learn myself in ROS or gazebo or pybullet.