stable-baselines3
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kaggle-environments | stable-baselines3 | |
---|---|---|
55 | 46 | |
273 | 7,894 | |
1.5% | 5.2% | |
6.6 | 8.2 | |
about 1 month ago | 5 days ago | |
Jupyter Notebook | Python | |
Apache License 2.0 | MIT License |
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
kaggle-environments
- Data Science Roadmap with Free Study Material
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Help needed! My first hackathon
If you are interested in Data Science, you may want to look at Kaggle competitions. https://www.kaggle.com/competitions
- What's a statistical / research methodology, that's not usually taught in grad programs, that you think more IO's should be aware about?
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Freaking out about how I’m inexperienced to land an internship and eventually a job
Secondly, if you feel like you do not have enough skills or a lack of practice answering problem statements, there are a lot of good websites where you can find interesting projects. I would recommend starting participating in some Kaggle competitions or download some free Google datasets and start playing with them.
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Capitalism provides half-assed solutions to extinction-level problems caused by capitalism
For reference: Kaggle is a Google product. You can see the list of current competitions here.
- Where can neural networks take me? - Semi-existential crisis
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What Can I Do With My Time as a Substitute for Strategy Computer Games?
You could try Kaggle competitions, or participating in forecasting markets (as you stated) is another option. You don't need any specific skill set to be a forecaster, the rules of the bet are stipulated and from there it's just based on your ability to predict the outcome. You could also try your hand at investing in the stock market, or try and make money betting on sports games. If you're very good at this stuff I'm sure you can make a lot of money doing it. The thing to keep in mind is that generally video games are much much easier than real life
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What is the best advanced professional certification for Data Science/ML/DL/MLOps?
As to the specifics of your projects, that's up to you. Try browsing Kaggle; check out some of the work we have on The Pudding; check out some journalism examples to see what you can try to build on or improve.
- Suggestions for projects on kaggle for cv?
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Hi! Im doing research on AI innovation. Does anybody know any specific platform where I can learn/understand and get case studies or on-going projects that companies are implementing? Thanks for your help!
You might want to look at kaggle competitions.
stable-baselines3
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Sim-to-real RL pipeline for open-source wheeled bipeds
The latest release (v3.0.0) of Upkie's software brings a functional sim-to-real reinforcement learning pipeline based on Stable Baselines3, with standard sim-to-real tricks. The pipeline trains on the Gymnasium environments distributed in upkie.envs (setup: pip install upkie) and is implemented in the PPO balancer. Here is a policy running on an Upkie:
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[P] PettingZoo 1.24.0 has been released (including Stable-Baselines3 tutorials)
PettingZoo 1.24.0 is now live! This release includes Python 3.11 support, updated Chess and Hanabi environment versions, and many bugfixes, documentation updates and testing expansions. We are also very excited to announce 3 tutorials using Stable-Baselines3, and a full training script using CleanRL with TensorBoard and WandB.
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[Question] Why there is so few algorithms implemented in SB3?
I am wondering why there is so few algorithms in Stable Baselines 3 (SB3, https://github.com/DLR-RM/stable-baselines3/tree/master)? I was expecting some algorithms like ICM, HIRO, DIAYN, ... Why there is no model-based, skill-chaining, hierarchical-RL, ... algorithms implemented there?
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Stable baselines! Where my people at?
Discord is more focused, and they have a page for people who wants to contribute https://github.com/DLR-RM/stable-baselines3/blob/master/CONTRIBUTING.md
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SB3 - NotImplementedError: Box([-1. -1. -8.], [1. 1. 8.], (3,), <class 'numpy.float32'>) observation space is not supported
Therefore, I debugged this error to the ReplayBuffer that was imported from `SB3`. This is the problem function -
- Exporting an A2C model created with stable-baselines3 to PyTorch
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Shimmy 1.0: Gymnasium & PettingZoo bindings for popular external RL environments
Have you ever wanted to use dm-control with stable-baselines3? Within Reinforcement learning (RL), a number of APIs are used to implement environments, with limited ability to convert between them. This makes training agents across different APIs highly difficult, and has resulted in a fractured ecosystem.
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Stable-Baselines3 v1.8 Release
Changelog: https://github.com/DLR-RM/stable-baselines3/releases/tag/v1.8.0
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[P] Reinforcement learning evolutionary hyperparameter optimization - 10x speed up
Great project! One question though, is there any reason why you are not using existing RL models instead of creating your own, such as stable baselines?
- Is stable-baselines3 compatible with gymnasium/gymnasium-robotics?
What are some alternatives?
CKAN - CKAN is an open-source DMS (data management system) for powering data hubs and data portals. CKAN makes it easy to publish, share and use data. It powers catalog.data.gov, open.canada.ca/data, data.humdata.org among many other sites.
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.
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
docarray - Represent, send, store and search multimodal data
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
datasci-ctf - A capture-the-flag exercise based on data analysis challenges
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
dremio-oss - Dremio - the missing link in modern data
tianshou - An elegant PyTorch deep reinforcement learning library.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros