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Well done. I really understood this in 30 minutes after going through bunch of notes and maths without really understand what was happening. Thanks very much
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You're welcome 😊 Stay connected with our channel and team :) . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Hi : ) We really are glad to hear this ! Truly feels good that our team is delivering and making your learning easier :) Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Hi : ) We really are glad to hear this ! Truly feels good that our team is delivering and making your learning easier :) Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Thanks for the compliment, Fathi! We are glad you loved the video. Do subscribe to the channel and hit the bell icon to never miss an update from us in the future. Cheers!
Hi Mamun, thank you for your suggestion. We will definitely come up with an exclusive tutorial for the same. Meanwhile, do subscribe to our channel and stay tuned. Cheers :)
Hey, There is a (1,1), (2,2), (3,3), (4,4), connectivity, but the reward to traverse from node 4 to 4 is zero. Because node 1,2,3,4 are not the goal nodes. Hope this helps. Cheers!
Here are some of the applications of Reinforcement Learning: 1. Robotics for industrial automation. 2. Business strategy planning. 3. Machine learning and data processing. 4. It helps you to create training systems that provide custom instruction and materials according to the requirement of students. 5. Aircraft control and robot motion control.
I liked the explanation and the flow of concepts but there are moments in this talk where the user (me/us) must ask, is the speaker instructing us based on an industry practice or on how this specific model is configured.. For example, when you say, the reward for an action that doesn't take you directly to the goal is zero .. do you mean that the goal is zero in this specific implementation or do you mean this is universally always that case. My brain gets hung up when the exact context isn't defined.
Hi Jeromy, thanks for watching the video. For each problem statement a different approach or a different model is built. So to answer your question, the instructor was referring to that particular problem statement. Hope this helps!
Hi Venky, thanks for the compliment! The iterations depends on the type of problem you're solving. Since this is a reinforcement learning problem, the agent requires more training because he must do everything from scratch.
Hi Akshay, we regret the error in your code. However, you can drop your email id in the comments and we shall assist you with the source codes. Hope this might be helpful, cheers :)
Halo, Great explanation but one doubt, I saw the code at the end.....are you using the same code to show the final Q matrix and path?.....because I am not getting the correct Q matrix and also the results are wrong!
Wow. Nicely Explained by the instructor. I thought Python has inbuilt algo for calculating Q Matrix. But looking to the python code, I realized that we need to code it. Am I right?
Hi Jose, Q(5,5) is zero initially because it represents the memory of the agent. On the other hand R(5,5) is 100 because it represents the reward the agent recieves on reaching the goal state (5).
Good to know our contents and videos are helping you learn better . We are glad to have you with us ! Please share your mail id to send the data sheets to help you learn better :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
I really appreciated the explanation and that you didn't use any ML-libraries. But in my case, where you have two objects, which randomly spawn on a grid-map at the beginning of the "Game". One object (the "agent") has to reach the other object ("the goal"). But I can't create a matching matrix in this kind of problem, right? So, how should I deal with it?
Hey, Glad you liked the content. Your 'goal' is not an agent. It can't span around in the grid because the goal is fixed. Are you suggesting that you want to create two machine learning agents? Can you please be more specific about it.
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Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Python Course curriculum, Visit our Website: bit.ly/2OpzQWw
That's why I love scratch implementation rather than using high end library, good job
Edureka is the modern education system ! We love you, keep on the great work specially the free content !
brilliant!! A perfect intro to ML. Well done Edureka!!
Best tutorial for reinforcement learning, well done. Thank u so much
Very good explanation
You are legend!!
Thank you!
This is the most beautiful think Ive seen today :)
This is one of the best lecture i have got to understand the crux of Q learning ,,hats off to you mam
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Excellent
One of the bestest learning source I have ever seen 🙄
Thank you for appreciating our efforts, Kan. Do subscribe, like and share to stay connected with us. Cheers :)
This video is better than Udacity nano degree ml program class on Reinforcement learning
I have never seen like this lecture in my entire life .expecting more video like this thank you
very well explained
Well done. I really understood this in 30 minutes after going through bunch of notes and maths without really understand what was happening. Thanks very much
Hey:) Thank you so much for your sweet words :) Really means a lot ! Glad to know that our content/courses is making you learn better :) Our team is striving hard to give the best content. Keep learning with us -Team Edureka :) Don't forget to like the video and share it with maximum people:) Do subscribe the channel:)
You explained it all in 46minutes. Thanks a lot!
You're welcome 😊 Stay connected with our channel and team :) . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
@@edurekaIN it was already done ✅
I did subscribe and hit the bell button 😊
Very good lecture, whoever was playing CS is a very good awper.....
Thank you I like it, happy day .
Very Useful and easy to understand - brilliant teacher , thank you !!
Hi : ) We really are glad to hear this ! Truly feels good that our team is delivering and making your learning easier :) Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Excellent explaination,really helpful..
Hi : ) We really are glad to hear this ! Truly feels good that our team is delivering and making your learning easier :) Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Wow :) Thanks edureka!
good explanation. thank you
Amazing video. Very well done, u managed to introduce a very technical matter into simple words. Tx for sharing
Thanks for the compliment, Fathi! We are glad you loved the video. Do subscribe to the channel and hit the bell icon to never miss an update from us in the future. Cheers!
it was really a great explanation . Thank you so much
At Video 19:00
Policy {A->C->D) = 15+ 50 = 65
Policy (A->B->C->D} = 30 + (-10) + 50 = 70
IS IT CORRECT? Please Clarify....
This was a great lecture.
Excellent and this is amazing to go through your video good job
What a fantastic video! Great work!!
Amazing!! It’s so clear now!
Simple but powerful explanation
Good explanation thank you 😘
awesome explanation
Superb tutorial
Happy with the explanation. Thank you so much .😊
amazing session
Very informative video,, thank you!
I like it! Super! Keep Going!
what a simple and wonderful lecture
Nice explanation. Short , accurate and practical.
Fantastic
please make a video of kalman filter with python.
Hi Mamun, thank you for your suggestion. We will definitely come up with an exclusive tutorial for the same. Meanwhile, do subscribe to our channel and stay tuned. Cheers :)
well explained !! thanks
Very nicely explained, best tutorial ill show to my university also how edureka teaches
ıt was so helpful . Thanks a lot:)
if there is a R(5,5) even though the end goal (room 5) is already reached, why is there no R(4,4), R(3,3), R(2,2) and R(1,1) ?
Hey, There is a (1,1), (2,2), (3,3), (4,4), connectivity, but the reward to traverse from node 4 to 4 is zero. Because node 1,2,3,4 are not the goal nodes. Hope this helps. Cheers!
What is the applications of reinforcement learning??
Here are some of the applications of Reinforcement Learning:
1. Robotics for industrial automation.
2. Business strategy planning.
3. Machine learning and data processing.
4. It helps you to create training systems that provide custom instruction and materials according to the requirement of students.
5. Aircraft control and robot motion control.
That was very good video. I am still learning. Thank You.
nice video
excellent
I liked the explanation and the flow of concepts but there are moments in this talk where the user (me/us) must ask, is the speaker instructing us based on an industry practice or on how this specific model is configured.. For example, when you say, the reward for an action that doesn't take you directly to the goal is zero .. do you mean that the goal is zero in this specific implementation or do you mean this is universally always that case. My brain gets hung up when the exact context isn't defined.
Hi Jeromy, thanks for watching the video. For each problem statement a different approach or a different model is built. So to answer your question, the instructor was referring to that particular problem statement. Hope this helps!
lucid explanation....I have a doubt....how can we decide the value of iterations?..the machine is intended to explore b those iterations?..
Hi Venky, thanks for the compliment! The iterations depends on the type of problem you're solving. Since this is a reinforcement learning problem, the agent requires more training because he must do everything from scratch.
Hats off
Nicely explained. But still I am getting an error in the code. Please guide me.
Hi Akshay, we regret the error in your code. However, you can drop your email id in the comments and we shall assist you with the source codes. Hope this might be helpful, cheers :)
Halo,
Great explanation but one doubt, I saw the code at the end.....are you using the same code to show the final Q matrix and path?.....because I am not getting the correct Q matrix and also the results are wrong!
Hey, The code creates and updates the Q matrix based on the movements of the agent. Can you please mention the error you are facing?
Wow. Nicely Explained by the instructor. I thought Python has inbuilt algo for calculating Q Matrix. But looking to the python code, I realized that we need to code it. Am I right?
Hi Chintan, thanks for watching the video. Yes, you need to write the code for Q Matrix.
Why the reward from minute 33.41 from Q(5,5) is not 100?
Hi Jose, Q(5,5) is zero initially because it represents the memory of the agent. On the other hand R(5,5) is 100 because it represents the reward the agent recieves on reaching the goal state (5).
@@edurekaIN okay, thank you
Present sir :)
sorry, how i get the code?
Hi Lia, kindly drop in your respective email id and we will share the code to you :)
please share code
Good to know our contents and videos are helping you learn better . We are glad to have you with us ! Please share your mail id to send the data sheets to help you learn better :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
I really appreciated the explanation and that you didn't use any ML-libraries. But in my case, where you have two objects, which randomly spawn on a grid-map at the beginning of the "Game". One object (the "agent") has to reach the other object ("the goal"). But I can't create a matching matrix in this kind of problem, right? So, how should I deal with it?
Hey, Glad you liked the content. Your 'goal' is not an agent. It can't span around in the grid because the goal is fixed. Are you suggesting that you want to create two machine learning agents? Can you please be more specific about it.
Very well explained.. 👍
Glad you liked it
Very clear explanation
Very good Explaination!..Thank You
We are super happy that Edureka is helping you learn better. Your support means a lot to us and it motivated us to create even better learning content and courses experience for you . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
very helpful!!
Very well explained. Thank you very much