gym
alphafold
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gym | alphafold | |
---|---|---|
96 | 35 | |
33,750 | 11,532 | |
0.8% | 2.1% | |
0.0 | 6.1 | |
about 1 month ago | 7 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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.
gym
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Shimmy 1.0: Gymnasium & PettingZoo bindings for popular external RL environments
This includes single-agent Gymnasium wrappers for DM Control, DM Lab, Behavior Suite, Arcade Learning Environment, OpenAI Gym V21 & V26. Multi-agent PettingZoo wrappers support DM Control Soccer, OpenSpiel and Melting Pot. For more information, read the release notes here:
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[P] Reinforcement learning evolutionary hyperparameter optimization - 10x speed up
how would this interact/compare with https://github.com/openai/gym?
- What has replaced OpenAI Retro Gym?
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Understanding Reinforcement Learning
If you'd like to learn more about reinforcement learning or play with a number of samples in controlled environments, I highly recommend you look at the documentation for OpenAI's Gym library and particularly the basic usage page. OpenAI's Gym provides a standardized environment for performing reinforcement learning on classic Atari games and a few other platforms and should be an educational resource. If you'd like a more detailed example, check out this tutorial on Paperspace's blog.
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Using the cross-entropy method to solve Frozen Lake
Frozen Lake is an OpenAI Gym environment in which an agent is rewarded for traversing a frozen surface from a start position to a goal position without falling through any perilous holes in the ice.
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How can we model an observation space of an env with different features and sizes.
After some googling, I have found that there are a wrappers for normalization (https://github.com/openai/gym/blob/master/gym/wrappers/normalize.py)
- RL Agent Library to use graph in spaces
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What is the "state of the art" in terms of game AI?
In regards to Competitive game AI the papers of OpenAi / Deepmind give you insight into what is coming: * Go: Alpha Go. * Dota: Open AI. * StarCraft: Alphastar. If you wanna have a go at it yourself try this: https://github.com/openai/gym.
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[N] Gym 0.26.0 was just released, with the last breaking changes to the core Gym API, and it will be stable going forward-- this is the stable version you want to finally upgrade all your things to
It’s has docs for like 9 months now: https://www.gymlibrary.dev/
Release notes available here: https://github.com/openai/gym/releases/tag/0.26.0
alphafold
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What is a recent scientific discovery that you find exciting?
For all you programmer types, these are the repos for each of them. AlphaFold - ProGen - ProtGPT2
- RFdiffusion: Diffusion model generates protein backbones
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Stability AI backs effort to bring machine learning to biomed
Their code/weights/everything.
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Is there any software that can predict if two amino acid sequences would interact?
Not sure what the chimerax plugin does, but you can run alphafold multimer yourself: https://github.com/deepmind/alphafold
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Top Github repo trends in 2021
No surprises here: deep learning is the most popular subcategory, with hugging face transformers repo, YOLOv5, Tensorflow and Deepmind’s Alphafold all in the mix. Surprisingly, the only proper infrastructure-ey repos on the list are Meilisearch and Clickhouse, a tad bit surprising given all the hype data infrastructure receives in VC-world, but again, probably just a question of size of end-user populations + whether data scientists spend tons of time on Github vs. Web Developers…
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AlphaGo: The Documentary
https://github.com/search?q=alphafold ... https://github.com/deepmind/alphafold
How do I reframe this problem in terms of fundamental algorithmic complexity classes (and thus the Quantum Algorithm Zoo thing that might optimize the currently fundamentally algorithmically computationally hard part of the hot loop that is the cost driver in this implementation)?
To cite in full from the MuZero blog post from December 2020: https://deepmind.com/blog/article/muzero-mastering-go-chess-... :
> Researchers have tried to tackle this major challenge in AI by using two main approaches: lookahead search or model-based planning.
> Systems that use lookahead search, such as AlphaZero, have achieved remarkable success in classic games such as checkers, chess and poker, but rely on being given knowledge of their environment’s dynamics, such as the rules of the game or an accurate simulator. This makes it difficult to apply them to messy real world problems, which are typically complex and hard to distill into simple rules.
> Model-based systems aim to address this issue by learning an accurate model of an environment’s dynamics, and then using it to plan. However, the complexity of modelling every aspect of an environment has meant these algorithms are unable to compete in visually rich domains, such as Atari. Until now, the best results on Atari are from model-free systems, such as DQN, R2D2 and Agent57. As the name suggests, model-free algorithms do not use a learned model and instead estimate what is the best action to take next.
> MuZero uses a different approach to overcome the limitations of previous approaches. Instead of trying to model the entire environment, MuZero just models aspects that are important to the agent’s decision-making process. After all, knowing an umbrella will keep you dry is more useful to know than modelling the pattern of raindrops in the air.
> Specifically, MuZero models three elements of the environment that are critical to planning:
> * The value: how good is the current position?
> * The policy: which action is the best to take?
> * The reward: how good was the last action?
> These are all learned using a deep neural network and are all that is needed for MuZero to understand what happens when it takes a certain action and to plan accordingly.
> Illustration of how Monte Carlo Tree Search can be used to plan with the MuZero neural networks. Starting at the current position in the game (schematic Go board at the top of the animation), MuZero uses the representation function (h) to map from the observation to an embedding used by the neural network (s0). Using the dynamics function (g) and the prediction function (f), MuZero can then consider possible future sequences of actions (a), and choose the best action.
> MuZero uses the experience it collects when interacting with the environment to train its neural network. This experience includes both observations and rewards from the environment, as well as the results of searches performed when deciding on the best action.
> During training, the model is unrolled alongside the collected experience, at each step predicting the previously saved information: the value function v predicts the sum of observed rewards (u), the policy estimate (p) predicts the previous search outcome (π), the reward estimate r predicts the last observed reward (u).
Libraries.io indexes software dependencies; but none are listed for the pypi:alphafold package: https://libraries.io/pypi/alphafold
The GitHub network/dependents view currently lists one repo that depends upon deepmind/alphafold: https://github.com/deepmind/alphafold/network/dependents
(Linked citations for science: How to cite a schema:SoftwareApplication in a schema:ScholarlyArticle , How to cite a software dependency in a dependency specification parsed by e.g. Libraries.io and/or GitHub. e.g. FigShare and Zenodo offer DOIs for tags of git repos.)
/?gscholar alphafold: https://scholar.google.com/scholar?q=alphafold
On a Google Scholar search result page, you can click "Cited by [ ]" to check which textual and/or URL citations gscholar has parsed and identified as indicating a relation to a given ScholarlyArticle.
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OpenAI Sold its Soul for $1B
> simply giving away everything for free
Which is what DeepMind has done with the AlphaFold code (Apache licensed https://github.com/deepmind/alphafold) and published model predictions (CC licensed at https://alphafold.ebi.ac.uk/). I guess they could publish the weights but that would probably be useless since nobody else would be running the exact same hardware.
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Structure prediction discussion (AlphaFold2, RoseTTAfold)
AlphaFold2 paper , GitHub
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AlphaFold 2 is here: what’s behind the structure prediction miracle
Well, AlphaFold 2 generates MSA by invoking things in Python: https://github.com/deepmind/alphafold/blob/main/alphafold/da.... So the article is actually mistaken on this point.
What are some alternatives?
RoseTTAFold - This package contains deep learning models and related scripts for RoseTTAFold
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.
carla - Open-source simulator for autonomous driving research.
tensorflow - An Open Source Machine Learning Framework for Everyone
dm_control - Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
open_spiel - OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
rlcard - Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO.
agents - TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
PaddlePaddle - PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
gensim - Topic Modelling for Humans