garage
nn-template
| garage | nn-template | |
|---|---|---|
| 5 | 4 | |
| 1,813 | 614 | |
| 0.4% | 1.6% | |
| 0.0 | 7.2 | |
| about 1 year ago | 7 months ago | |
| Python | Python | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
garage
- Are there any follow-up studies of RL^2 algorithms?
- Which python library to pick for RL as a beginner
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Actor-critic reinforce-style gradient with entropy regularization vs soft actor-critic
max: https://github.com/rlworkgroup/garage/blob/62bbc5cec70480e3bf2039cea7f130befecbef10/src/garage/torch/algos/vpg.py#L158 regularized: https://github.com/rlworkgroup/garage/blob/62bbc5cec70480e3bf2039cea7f130befecbef10/src/garage/torch/algos/vpg.py#L343
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Do you have any recommendation on a Reinforcement Learning library for Python?
TF-Agents and Garage look interesting and would be my first stop. Unfortunately I picked OpenAI baselines (not stable baselines) but it isn't supported any more.
- How to do unit testing for reinforcement learning
nn-template
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What libs/boiler plate/platforms do you use to abstract and optimize your workflow when starting a new project? [D]
If I was starting a new project, Iād like to try using this cookiecutter template: https://github.com/grok-ai/nn-template
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MLOps stack using Pycaret
I would pick a project that interests you as that'll help you power through. If there's nothing that comes to mind, image classification is fairly standard. PyCaret does a lot but if you want to understand each of the tools you've listed, I'd recommend tackling each one separately. That being said, I don't think there's anything wrong starting with using a high level library and diving deeper as the need arises. If you do decide to build it piece by piece, it sometimes useful to have a library that'll help you start and remove the boilerplate of all of these tools. I came across a template repo which has a bunch of the tools you've listed which could be a good starting point: https://github.com/lucmos/nn-template
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[P] Generic template to bootstrap your PyTorch project with PyTorch Lightning, Hydra, W&B, DVC, and Streamlit
Link: https://github.com/lucmos/nn-template
- Template to bootstrap your project with PyTorch Lightning, Hydra, W&B and DVC
What are some alternatives?
lightning-hydra-template - PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ā”š„ā”
lightning-hydra-template - Deep Learning project template best practices with Pytorch Lightning, Hydra, Tensorboard.
Metaworld - Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning
metaworld - Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning [Moved to: https://github.com/Farama-Foundation/Metaworld]
pytorch_tempest - My repo for training neural nets using pytorch-lightning and hydra
VMAgent - Our VMAgent is a platform for exploiting Reinforcement Learning (RL) on Virtual Machine (VM) scheduling tasks.
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rl_lib - Series of deep reinforcement learning algorithms š¤
fds - Fast Data Science, AKA fds, is a CLI for Data Scientists to version control data and code at once, by conveniently wrapping git and dvc
agents - TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
mdx-net - KUIELAB-MDX-Net got the 2nd place on the Leaderboard A and the 3rd place on the Leaderboard B in the MDX-Challenge ISMIR 2021