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Top 23 reinforcement-learning Open-Source Projects
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nn
🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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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.
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applied-ml
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
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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.
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ml-agents
The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
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reinforcement-learning-an-introduction
Python Implementation of Reinforcement Learning: An Introduction
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WorkOS
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Bullet
Bullet Physics SDK: real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics, machine learning etc.
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deep-learning-drizzle
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
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FinGPT
FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
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awesome-artificial-intelligence
A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.
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amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
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vowpal_wabbit
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
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wandb
🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
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stable-baselines3
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
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PaLM-rlhf-pytorch
Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM
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Gymnasium
An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
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SaaSHub
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course Computer science is very wast field the fundamental remains same, learn basic fundamentals, data structures, concepts of object oriented programming.
22. Ray | Github | tutorial
Project mention: How do I change the maximum number of steps for training | /r/MLAgents | 2023-12-07
For typical game physics engines... not that much. Math libraries like Eigen or Blaze use lots of template metaprogramming techniques under the hood that can help when you're doing large batched matrix multiplications (since it can remove temporary allocations at compile-time and can also fuse operations efficiently, as well as applying various SIMD optimizations), but it doesn't really help when you need lots of small operations (with mat3 / mat4 / vec3 / quat / etc.). Typical game physics engines tend to use iterative algorithms for their solvers (Gauss-Seidel, PBD, etc...) instead of batched "matrix"-oriented ones, so you'll get less benefits out of Eigen / Blaze compared to what you typically see in deep learning / scientific computing workloads.
The codebases I've seen in many game physics engines seem to all roll their own math libraries for these stuff, or even just use SIMD (SSE / AVX) intrinsics directly. Examples: PhysX (https://github.com/NVIDIA-Omniverse/PhysX), Box2D (https://github.com/erincatto/box2d), Bullet (https://github.com/bulletphysics/bullet3)...
Project mention: GPT-4, without specialized training, beat a GPT-3.5 class model that cost $10B | news.ycombinator.com | 2024-03-24There is also the open source FinGPT, that is claimed to beat GPT4 in some benchmarks at a fine tuning cost of $17.25.
I need to use AWS Sagemaker (required, can't use easier services) and my adviser gave me this document to start with: https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/question_answering_retrieval_augmented_generation/question_answering_langchain_jumpstart.ipynb
"deep neural network" https://github.com/Hvass-Labs/TensorFlow-Tutorials
Project mention: A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev | dev.to | 2024-02-05Weights & Biases — The developer-first MLOps platform. Build better models faster with experiment tracking, dataset versioning, and model management. Free tier for personal projects only, with 100 GB of storage included.
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?
and the implementation https://github.com/google/trax/blob/master/trax/models/resea... if you are interested.
Hope you get to look into this!
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:
Project mention: How should I get an in-depth mathematical understanding of generative AI? | /r/datascience | 2023-05-18ChatGPT isn't open sourced so we don't know what the actual implementation is. I think you can read Open Assistant's source code for application design. If that is too much, try Open Chat Toolkit's source code for developer tools . If you need very bare implementation, you should go for lucidrains/PaLM-rlhf-pytorch.
"Show HN: Ghidra Plays Mario" (2023) https://news.ycombinator.com/item?id=37475761 :
[RL, MuZero reduxxxx ]
> Farama-Foundation/Gymnasium is a fork of OpenAI/gym and it has support for additional Environments like MuJoCo: https://github.com/Farama-Foundation/Gymnasium#environments
> Farama-Foundatiom/MO-Gymnasiun: "Multi-objective Gymnasium environments for reinforcement learning": https://github.com/Farama-Foundation/MO-Gymnasium
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
The latest post mention was on 2024-04-17.
reinforcement-learning related posts
- Bayesianbandits: A Pythonic microframework for multi-armed bandit problems
- Adding Weapons
- Understand how transformers work by demystifying all the math behind them
- Show HN: An end-to-end reinforcement learning library for infinite horizon tasks
- Show HN: Easily train AlphaZero-like agents on any environment you want
- trading-bot: Implementation of deep reinforcement learning using Deep Q Network (DQN). Only supports single security at the moment. Idea is roughly based [here](https://keon.github.io/deep-q-learning/) and uses tensorflow/keras. Interesting helper py
- TradeMaster: NEW Deep Learning And Reinforcement Learning - star count:910.0
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A note from our sponsor - WorkOS
workos.com | 19 Apr 2024
Index
What are some of the best open-source reinforcement-learning projects? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | cs-video-courses | 64,694 |
| 2 | nn | 47,503 |
| 3 | Ray | 30,988 |
| 4 | applied-ml | 25,853 |
| 5 | d2l-en | 21,564 |
| 6 | ml-agents | 16,295 |
| 7 | reinforcement-learning-an-introduction | 13,154 |
| 8 | Bullet | 11,862 |
| 9 | deep-learning-drizzle | 11,738 |
| 10 | FinGPT | 11,334 |
| 11 | awesome-artificial-intelligence | 9,587 |
| 12 | amazon-sagemaker-examples | 9,491 |
| 13 | TensorFlow-Tutorials | 9,250 |
| 14 | vowpal_wabbit | 8,400 |
| 15 | wandb | 8,159 |
| 16 | machine_learning_examples | 8,072 |
| 17 | trax | 7,948 |
| 18 | pysc2 | 7,904 |
| 19 | stable-baselines3 | 7,850 |
| 20 | PaLM-rlhf-pytorch | 7,587 |
| 21 | TensorLayer | 7,275 |
| 22 | Practical_RL | 5,702 |
| 23 | Gymnasium | 5,651 |
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