@@tyucelen I have taken your MRAC course and applied it to my Quadcopter project. While the input values (U1) I am obtaining are accurate, their range seems unpredictable. I am facing difficulty in mapping these values for motor speed since I do not know the maximum and minimum values of the input. Could you please guide me on how to approach this issue or suggest any method how to bound the input values?
Great sir. You have talked about Reinforcement learning in Continuous environment like neural networks and etc Does all this exist in Discrete time too ? Would you also cover multi-agent systems too ?
Thanks a lot 🎉
Great
Thank you 🌸🙏
Have been waited your videos so long
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Another great series. Thank you Sir
So nice of you 🤘
Fİnally new video !! 🎉🎉
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Happy to learn with you again sir
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@@tyucelen I have taken your MRAC course and applied it to my Quadcopter project. While the input values (U1) I am obtaining are accurate, their range seems unpredictable. I am facing difficulty in mapping these values for motor speed since I do not know the maximum and minimum values of the input.
Could you please guide me on how to approach this issue or suggest any method how to bound the input values?
@@konkalavenkateswarluredd-su8tj Frankly, I don't know your system details so I have to pass this question. Good luck.
Çok sağolun hocam.
👍🙏
Great sir. You have talked about Reinforcement learning in Continuous environment like neural networks and etc
Does all this exist in Discrete time too ?
Would you also cover multi-agent systems too ?
Continuous environment means that the agent does not travel from one box to another as in discrete environment