Serpent.AI VS Pytorch

A: I am. While serpent.ai attempted to get an AI to play Isaac, the project hasn't been updated in years.

You don't need a whole team nowadays. Amazon has services that let you train your own neural nets with a little bit of knowledge. Then there are tools like SerpentAI that let your AI interface with games (don't know if it works with Blizzard games, but it works with Steam).

i cant. is there any way https://github.com/SerpentAI/SerpentAI would work. the game is entirely mouse movements.

Hey! So, I'd like to implement NEAT machine learning to train an agent to play Donkey Kong Country, but there doesn't seem to be much in the way of tutorials/examples for Serpent.AI (like, its weirdly dead given how powerful it seems to be and github page is full of dead links) so I wanted to see if any of you fine folk would recommend for/against its use or that of an alternative. Any other advice also appreciated.

Serpent AI : Game Agent Framework. Helping you create AIs / Bots to play any game you own! BETA

> does PyTorch have a similar concept

of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...

fyi should work on most 40xx[1]

[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...

Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...

Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....

> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].

*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.

In PyTorch with @, dot() or matmul():

import torch # we use PyTorch: https://pytorch.org data = torch.tensor(encode(text), dtype=torch.long) print(data.shape, data.dtype) print(data[:1000]) # the 1000 characters we looked at earlier will to the GPT look like this

AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.

Which label applies to a tool sometimes depends on what you do with it. For example, PyTorch or TensorFlow can be called a library, a toolkit, or a machine-learning framework.

In PyTorch with * or mul(). ` or mul()` can multiply 0D or more D tensors by element-wise multiplication:

When I was at Facebook, I wrote a Python script to extract shell scripts from GitHub Actions workflows, so we could run them all through ShellCheck: https://github.com/pytorch/pytorch/blob/69e0bda9996865e319db...