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features
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support

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Description

Gymnasium serves as a well-maintained alternative to OpenAI’s Gym library, offering a standardized API for reinforcement learning alongside a wide variety of reference environments. Its interface is designed to be user-friendly and pythonic, effectively accommodating a range of general RL challenges while also providing a compatibility layer for older Gym environments. Central to Gymnasium is the Env class, a robust Python construct that embodies the principles of a Markov Decision Process (MDP) as described in reinforcement learning theory. This essential class equips users with the capability to generate an initial state, transition through various states in response to actions, and visualize the environment effectively. In addition to the Env class, Gymnasium offers Wrapper classes that enhance or modify the environment, specifically targeting aspects like agent observations, rewards, and actions taken. With a collection of built-in environments and tools designed to ease the workload for researchers, Gymnasium is also widely supported by numerous training libraries, making it a versatile choice for those in the field. Its ongoing development ensures that it remains relevant and useful for evolving reinforcement learning applications.

Description

You can develop on your laptop, then scale the same Python code elastically across hundreds or GPUs on any cloud. Ray converts existing Python concepts into the distributed setting, so any serial application can be easily parallelized with little code changes. With a strong ecosystem distributed libraries, scale compute-heavy machine learning workloads such as model serving, deep learning, and hyperparameter tuning. Scale existing workloads (e.g. Pytorch on Ray is easy to scale by using integrations. Ray Tune and Ray Serve native Ray libraries make it easier to scale the most complex machine learning workloads like hyperparameter tuning, deep learning models training, reinforcement learning, and training deep learning models. In just 10 lines of code, you can get started with distributed hyperparameter tune. Creating distributed apps is hard. Ray is an expert in distributed execution.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Python Yes 
Amazon EC2 Trn2 Instances No 
Amazon EKS No 
Amazon SageMaker No 
Amazon Web Services (AWS) No 
Anyscale No 
Apache Airflow No 
Azure Kubernetes Service (AKS) No 
Dask No 
Databricks No 
Feast No 
Flyte No 
Google Kubernetes Engine (GKE) No 
Kubernetes No 
LanceDB No 
MLflow No 
PyTorch No 
Snowflake No 
TensorFlow No 
Union Cloud No 

Integrations

Python Yes 
Amazon EC2 Trn2 Instances Yes 
Amazon EKS Yes 
Amazon SageMaker Yes 
Amazon Web Services (AWS) Yes 
Anyscale Yes 
Apache Airflow Yes 
Azure Kubernetes Service (AKS) Yes 
Dask Yes 
Databricks Yes 
Feast Yes 
Flyte Yes 
Google Kubernetes Engine (GKE) Yes 
Kubernetes Yes 
LanceDB Yes 
MLflow Yes 
PyTorch Yes 
Snowflake Yes 
TensorFlow Yes 
Union Cloud Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Open source. Consumption-based.
Free Trial Yes 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Vendor Details

Company Name

Gymnasium

Country

United States

Website

gymnasium.farama.org

Vendor Details

Company Name

Anyscale

Founded

2019

Country

United States

Website

ray.io

Product Features

Product Features

Deep Learning

Convolutional Neural Networks No 
Document Classification No 
Image Segmentation No 
ML Algorithm Library No 
Model Training No 
Neural Network Modeling No 
Self-Learning No 
Visualization No 

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

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