python-recsys
gym
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| python-recsys | gym | |
|---|---|---|
| 0 | 96 | |
| 1,468 | 33,819 | |
| - | 0.7% | |
| 0.0 | 0.0 | |
| over 3 years ago | 10 days ago | |
| Python | Python | |
| http://www.gnu.org/copyleft/gpl.html | GNU General Public License v3.0 or later |
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python-recsys
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Tracking mentions began in Dec 2020.
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?
Surprise - A Python scikit for building and analyzing recommender systems
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）
Crab - Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (numpy, scipy, matplotlib).
Keras - Deep Learning for humans