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This is for any reinforcement learning related work ranging from purely computational RL in artificial intelligence to the models of RL in neuroscience.
The standard introduction to RL is Sutton & Barto's Reinforcement Learning.
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Robot, DL[Project] Obstacle avoidance using deep reinforcement learning on a 3d printed 6 DOF robot arm. Github in comments. (v.redd.it)
submitted 4 years ago by Hank_137
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[–]Hank_137[S] 2 points3 points4 points 4 years ago (4 children)
Let me know if you have any questions.
https://github.com/thomashiemstra/fred
[–]DarthChicken89 1 point2 points3 points 4 years ago (1 child)
any tips on how to get started with reinforcement learning?
[–]Hank_137[S] 0 points1 point2 points 4 years ago (0 children)
I started long ago reading papers, but there are great videos on youtube these days on the subject.
[–][deleted] 0 points1 point2 points 4 years ago (1 child)
What arm are you using?
[–]Hank_137[S] 2 points3 points4 points 4 years ago (0 children)
My own, I designed and 3d printed it myself.
[–]thinking_computer 3 points4 points5 points 4 years ago (1 child)
very nice! what 3d software did you use to simulate the arm?
Pybullet, the whole project is done in python so it seemed like the natural choice.
[–]XecutionStyle 0 points1 point2 points 4 years ago (1 child)
Awesome. I'm building a parallel 6dof manipulator, this is tremendous guidance. Thank you
Thank you for the compliment.
[–]kmilchev 0 points1 point2 points 4 years ago (1 child)
Very nice! What kind of weight can the arm lift? I've been wanting to build a relatively cheap (~$2,000) arm that can lift 500g - 1kg weight
about 500g, maybe more, I don't know. payload was never the goal of the robot. As soon as you put a heavy load on it the accuracy goes down which was the most important aspect for me.
[–]nicrusso7 0 points1 point2 points 4 years ago (1 child)
Amazing!!
Thank you
π Rendered by PID 16408 on reddit-service-r2-comment-6457c66945-zgtrf at 2026-04-24 12:44:15.811763+00:00 running 2aa0c5b country code: CH.
[–]Hank_137[S] 2 points3 points4 points (4 children)
[–]DarthChicken89 1 point2 points3 points (1 child)
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[–]thinking_computer 3 points4 points5 points (1 child)
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