crab
gym
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crab | gym | |
---|---|---|
37 | 96 | |
5,061 | 33,750 | |
0.6% | 0.8% | |
9.9 | 0.0 | |
2 months ago | about 1 month ago | |
Rust | Python | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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.
crab
- Rust language forked by community into Crab
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Rust has been forked to the Crab Language
Even more context: https://github.com/crablang/crab/issues/14
If that’s what happens, it’s not a great outcome. Foundation changes policy, restricting the use of a mark. The community responds by… ceasing to use the mark. That’s what they were asking for!
If a whole slew of things were only available in Crab, the Rust trade mark would be devalued. That would be the one coherent theory of why you would launch such a project and try to get others on board. And it is why Ashley G Williams was commenting in that Register piece on the lack of technical talent (“language designers”) that had jumped ship. Commitment of talent and effort and resources is by and large what makes the trade mark valuable. People who are important to the project leaving is the only useful measure of an effective protest.
Since the Crab project fails to mention any specific people who have signed on, or even who decided to create it, I don’t see it having any impact whatsoever. The Rust Foundation will not feel threatened by this. I suspect the maximum it can be is just another IceWeasel. That is certainly the vision laid out by this person on one of the issues, who despite posting as if they created it, is careful to disclaim any responsibility for the project or to call any of the decisions their own. (Come on!) https://github.com/crablang/crab/issues/14#issuecomment-1508...
It’s also the vision laid out on the website: “promoting the language without worrying about the litigation associated with trademark infringement.” Basically the project has outlined the least ambitious possible goals and apparently nobody is willing to sign their name on it. My advice is to write an open letter and open it for signatures instead.
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On the RustConf Keynote
I think https://github.com/crablang/crab is the healthy path forward.
- Why I Left Rust
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JT: Why I left Rust
Crablang is something I spotted further up the comments.
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CrabLang
Is it ok to include the copyright message from Rust including references to Rust? https://github.com/crablang/crab/blob/master/COPYRIGHT
What about attribution?
There may or may not have been an issue open for this: https://github.com/crablang/crab/issues/5
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Should I start renaming my crate repositories that have "rust" in their names?
Lol we had a similar joke about that: https://github.com/crablang/crab/issues/5
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
What are some alternatives?
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
AirSim - Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research