gym VS alphafold

Compare gym vs alphafold and see what are their differences.

gym

A toolkit for developing and comparing reinforcement learning algorithms. (by openai)

alphafold

Open source code for AlphaFold. (by google-deepmind)
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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
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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

Posts with mentions or reviews of gym. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-04-25.

alphafold

Posts with mentions or reviews of alphafold. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-05-07.
  • What is a recent scientific discovery that you find exciting?
    2 projects | /r/AskScienceDiscussion | 7 May 2023
    For all you programmer types, these are the repos for each of them. AlphaFold - ProGen - ProtGPT2
  • RFdiffusion: Diffusion model generates protein backbones
    3 projects | news.ycombinator.com | 30 Mar 2023
  • Stability AI backs effort to bring machine learning to biomed
    2 projects | /r/singularity | 5 Nov 2022
    Their code/weights/everything.
  • Is there any software that can predict if two amino acid sequences would interact?
    3 projects | /r/bioinformatics | 6 May 2022
    Not sure what the chimerax plugin does, but you can run alphafold multimer yourself: https://github.com/deepmind/alphafold
  • Top Github repo trends in 2021
    47 projects | dev.to | 12 Jan 2022
    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…
  • AlphaGo: The Documentary
    4 projects | news.ycombinator.com | 12 Sep 2021
    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).

    4 projects | news.ycombinator.com | 12 Sep 2021
    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.

  • OpenAI Sold its Soul for $1B
    2 projects | news.ycombinator.com | 4 Sep 2021
    > 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.

  • Structure prediction discussion (AlphaFold2, RoseTTAfold)
    2 projects | /r/ProteinDesign | 22 Jul 2021
    AlphaFold2 paper , GitHub
  • AlphaFold 2 is here: what’s behind the structure prediction miracle
    3 projects | news.ycombinator.com | 20 Jul 2021
    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?

When comparing gym and alphafold you can also consider the following projects:

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