q-learning-algorithms
machine_learning_examples
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| q-learning-algorithms | machine_learning_examples | |
|---|---|---|
| 1 | 3 | |
| 4 | 8,072 | |
| - | - | |
| 0.0 | 5.3 | |
| almost 3 years ago | 22 days ago | |
| Python | Python | |
| - | - |
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q-learning-algorithms
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actor-critic algorithms
I learn quite some things about reinforcement learning in the last months, and I feel like I understand much better deep-Q learning algorithms (if you want, you can check my [repo](https://github.com/thomashirtz/q-learning-algorithms). I would like to change a little bit my focus towards actor-critics algorithms now. The only thing is, I feel like in comparison to Q-learning algorithms, the explanations of the papers are not as precise as for Q-learning, and explanations on the internet diverge really greatly (e.g. the original paper does not give the A2C but only the A3C for one learner).
machine_learning_examples
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Doubt about numpy's eigen calculation
Does that mean that the example I found on the internet is wrong (I think it comes from a DL Course, so I'd imagine it is not wrong)? or does it mean that I am comparing two different things? I guess this has to deal with right and left eigen vectors as u/JanneJM pointed out in her comment?
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How to save an attention model for deployment/exposing to an API?
I've been following a course teaching how to make an attention model for neural machine translation, This is the file inside the repo. I know that I'll have to use certain functions to make the textual input be processed in encodings and tokens, but those functions use certain instances of the model, which I don't know if I should keep or not. If anyone can please take a look and help me out here, it'd be really really appreciated.
What are some alternatives?
bomberland - Bomberland: a multi-agent AI competition based on Bomberman. This repository contains both starter / hello world kits + the engine source code
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
chess - Program for playing chess in the console against AI or human opponents
applied-ml - 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
AgileRL - Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
fragile - Framework for building algorithms based on FractalAI
neptune-client - 📘 The MLOps stack component for experiment tracking
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
dm_env - A Python interface for reinforcement learning environments