So glad I discovered this channel, you make such good quality videos and explain the topic so well. Looking forward to the next episode of 'Understanding Unity ML-Agents'. Hope you are doing well, all the best!
Multi modal ai additions to the neural network, id say link a perceiver, GPT-3 and apply unitys computer vision, like if there were basic npcs around that environment with scripts or GPT-3, for the ml one to learn helpful info from in catching the dog, and putting different ways to catch the dog
Training something like this will take time and patience btw, at this point all u need is a language modal that reacts with the npc, and you have created a baby virtual human 😁 The clockwork mage strikes again
@@ClockworksGames sir I know that you used deep reinforcement learning only but I am asking what type of deep reinforcement learning algorithm like DQN,PPO ,A2C,A3C...
@@VishnuVardhan-vy1ve Ok, thanks for the clarification on what you are asking. The ML-Agents toolkit uses PPO by default and also supports SAC. I was using PPO. See the docs for more information: github.com/Unity-Technologies/ml-agents/blob/release_17/docs/ML-Agents-Overview.md
Um, ya, but i got an idea 💡 Do a needs based state machine 🤔and have it designed for the ml agent to use as little input as necessary to function in an everyday living environment, everyday events, items to interact with, it would be alot, but a base model would be worth $ if u got that working, basically a sim with a GPT-3 to talk with, a perceiver ai modal linking GPT-3 and ml agents,
It does seem that ML-Agents is best for one specific goal, if that is what you mean, and that some logic at a higher level is needed if there are multiple goals.
@@ClockworksGames ya basically, but it can handle multiple, but less for the "mind" to still get a clear path to the optimal solution for the task But lets say 1 Interact with objects would be the action that would then inform effect the hard coded state machine stuff i told u about
Thank you for showing this experiment it was interesting, looking forward to future updates.
Thank you for the feedback!
Look forward to seeing how this works out.
Thanks for the comment! More videos coming soon.
So glad I discovered this channel, you make such good quality videos and explain the topic so well. Looking forward to the next episode of 'Understanding Unity ML-Agents'. Hope you are doing well, all the best!
Thanks very much for this comment! I really appreciate the feedback. I am working on the next video now...
What will be your approach to solve the issue of jerking movement of the humanoid
Please see the videos I made after this one, especially: ruclips.net/video/nIozvpOsb0Q/видео.html.
Multi modal ai additions to the neural network, id say link a perceiver, GPT-3 and apply unitys computer vision, like if there were basic npcs around that environment with scripts or GPT-3, for the ml one to learn helpful info from in catching the dog, and putting different ways to catch the dog
🤩 cool
Training something like this will take time and patience btw, at this point all u need is a language modal that reacts with the npc, and you have created a baby virtual human 😁
The clockwork mage strikes again
Idk, its alot as u know about ai, but i loved the progress, any item interactions?
No item interactions yet, other than "finding" (actually touching) the dog.
@@ClockworksGames thats easy fix, small proximity trigger
Sir I had a doubt .. that can I know the name of algrothim used in this video
The video uses Deep Reinforcement Learning implemented by the ML-Agents toolkit. Does that answer your question?
@@ClockworksGames sir I know that you used deep reinforcement learning only but I am asking what type of deep reinforcement learning algorithm like DQN,PPO ,A2C,A3C...
@@VishnuVardhan-vy1ve Ok, thanks for the clarification on what you are asking. The ML-Agents toolkit uses PPO by default and also supports SAC. I was using PPO. See the docs for more information: github.com/Unity-Technologies/ml-agents/blob/release_17/docs/ML-Agents-Overview.md
@@ClockworksGames thanks sir
Also sorry i blast comments i get excited about this stuff, lol
Um, ya, but i got an idea 💡
Do a needs based state machine 🤔and have it designed for the ml agent to use as little input as necessary to function in an everyday living environment, everyday events, items to interact with, it would be alot, but a base model would be worth $ if u got that working, basically a sim with a GPT-3 to talk with, a perceiver ai modal linking GPT-3 and ml agents,
Interesting and ambitious ideas...
@@ClockworksGames lol, my study of humanoid ai might be useful in concept design 😄
Make the most humanoid npc possible before applying ml agents, but let the hard coded stuff be choosing which the ai would do
It does seem that ML-Agents is best for one specific goal, if that is what you mean, and that some logic at a higher level is needed if there are multiple goals.
@@ClockworksGames ya basically, but it can handle multiple, but less for the "mind" to still get a clear path to the optimal solution for the task
But lets say 1
Interact with objects would be the action that would then inform effect the hard coded state machine stuff i told u about