A.I. and Machine Learning for everybody! Sometimes I am teaching machines how to play games via Reinforcement Learning in Unity3D and sometimes I am exploring the deep history of A.I.
I have always had a question about mlagents: they randomly select actions at the beginning of training. Can we incorporate human intervention into the training process of mlagents to make them train faster? Is there a corresponding method in mlagents? Looking forward to your answer.
I have always had a question about mlagents: they randomly select actions at the beginning of training. Can we incorporate human intervention into the training process of mlagents to make them train faster? Is there a corresponding method in mlagents? Looking forward to your answer.
I have always had a question about mlagents: they randomly select actions at the beginning of training. Can we incorporate human intervention into the training process of mlagents to make them train faster? Is there a corresponding method in mlagents? Looking forward to your answer.
I have always had a question about mlagents: they randomly select actions at the beginning of training. Can we incorporate human intervention into the training process of mlagents to make them train faster? Is there a corresponding method in mlagents? Looking forward to your answer.
Thank you very much for your work and for creating such a video that is beneficial for beginners! I have a few questions that I would like you to answer: (1) How long have you trained? Can we upload the tensorboard file for training so that we can refer to how the tensorboard parameters should change during a good training process? (2) Assuming there are 24 cars in your original environment, can I accelerate the training using the -- env and -- num envs=2 parameters? Is it equivalent to training 48 vehicles simultaneously? Looking forward to your answer, thank you!
Wow, your project has really amazed me! May I ask if there is a GitHub project address for us to view the code? We don't know how to handle the fact that the obstacles in each scene are different?
The photo you show between 1:13 and 1:23 isn't Rosenblatt's "Mark I Perceptron". It's the IBM "Harvard Mark I" or "Automatic Sequence Controlled Calculator (ASCC)", a general-purpose electromechanical digital computer installed in 1944 and dismantled in 1959 (according to Wikipedia). Quite different from the Perceptron.
Hi Again! I am have made a hide and seek environment with a single seeker agent and a single hider agent. What I would like to do is to use the ray perception sensor 3d to give rewards depending if the hider is in the seekers view or not. What should I use for this, as the resources for the subject are rather scarce. Also, are the tags used in any way? for example if the seeker sensor hits the hider sensor(tag 2) then the seeker gains rewards and respectively the hider loses rewards
When we upload the image of Churros, does the dimension has to be 224 x 224? Coz I just uploaded churros image of different dimension and it is giving me Samosa answer even though it is churro pic.
According to GPT lol this behavior was only the result of stimulus through the photo-sensor and perhaps a reverse to be triggered by the contacts meeting when it bumped into an obstacle. I was like, well how would it have "known" to steer towards the light? It said that it was designed in such a way that if light was shining towards it from the side, it would just cause one motor to receive more voltage than the other and therefore uneven torque favoring one side. GPT is so remarkable. Anyhow, is this correct? I find this really cool.
Yes that sort of true they can't learn beam robotics followed this idea of thinking into more modern day, their walkers were quiet capable using motors hooked directly to a oscillation circuit. If a leg gets stuck for instance it will increase the motors resistance and slow down the oscillation giving that leg more time to get out giving it a simple walking adaptation system and some rare ones can store information in a capacitor. As for them not being AI it is hard to say what level intelligence starts and it just being stimulus don't forget the human brain is an analogue device so neural networks are making digital systems act as an anologue system so anologue stimulus approach has come back in a way.
Great video and great project, really enjoyed going through your project, very insightful. So I need some help, I am training my agent for almost a month now (as in training stopping and trying something else), I can't seem to get to the max level, the best agent I trained simply stops me from wining, but rarely go for the win, like if he has two in a row, and you have two in a row, he blocks you instead of wining, and I get to this point on the first 700k ish steps, if I trained longer, the agent goes nuts and is no more an AI and more like I do 1 then 2 then 3 player. I would appreciate the help if you gave me some advices or anything you done under the hood with your agents. And you mentioned in the video that your AI isn't perfect, any idea on that ? can't I reach a perfect player with self-play ?
I was waiting for the quote attribution... to say that this person is really unaware of ChatGPT and… well, unaware of AI, but then it hit me: the author is ChatGPT itself, brilliantly illustrating the paradox of being both aware and unaware of its own existence. How ironic!
