The Best ViZDoom Alternatives in 2026

DI-engine provides an OpenDILab framework for reinforcement learning projects.

TF-Agents provides a TensorFlow library for contextual bandits and reinforcement learning.

The somoclu project implements self-organizing maps in parallel across CPUs, GPUs, and clusters.

OpenMetricLearning provides metric learning pipelines, models, and a model zoo.

Use Learnergy, a Python library for training and working with energy-based machine learning models.

Karate Club offers algorithms for unsupervised learning on graph data in Python.

FEDOT automates machine learning model development through its modeling framework.

Enables single machine or distributed training and evaluation of deep learning models.

Detecto supports building and training computer vision models with the PyTorch machine learning framework.

This metric-learn repository contains Python implementations of metric learning algorithms.

Katib automates machine learning on Kubernetes, including hyperparameter tuning and neural architecture search.

hyperopt-sklearn uses hyperparameter optimization to select machine learning algorithms for scikit-learn.

metric-learn provides Python implementations of metric learning algorithms.

ML_Forgex trains, evaluates, and predicts with machine learning models on cleaned datasets.

PyTorch Geometric Temporal provides models and tools for spatiotemporal signal processing with neural learning.

ML-Ensemble provides Python tools for high-performance ensemble learning with scikit-learn.

Auto_ViML builds multiple machine learning models with a single line of code.

AutoGL provides automated graph learning through a machine learning framework and toolkit.

Lightwood builds and customizes machine learning pipelines using a declarative JSON-AI syntax.

DeepSpeed optimizes distributed training and inference for deep learning models.

Deeplearning4j provides tools for training and deploying deep learning models in the JVM.

Guild AI automates and optimizes machine learning experiments while tracking each run.

m2cgen converts machine learning models into native code for multiple programming languages without runtime dependencies.

EvalML builds, optimizes, and evaluates machine learning pipelines with automated data checks and objective functions.