Projects sharing features with Keras Rl

Sonnet is a modular machine learning framework and TensorFlow library used for building, training, and managing deep learning models. It functions as a system for composing neural networks from reusable modules and layers that encapsulate their own parameters and internal states. The project provides specialized tools for distributed model training, enabling the synchronization of gradients across multiple hardware devices. It also serves as a model state management system, allowing for the persistence of neural network weights and the export of portable models that separate the computation g

TensorFlow Reinforcement Learning

TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.

Ignite is a high-level training framework for PyTorch neural networks that serves as a training engine and deep learning lifecycle manager. It provides a structured system for organizing and automating training and evaluation loops, managing data iterators and triggering event handlers at specific milestones during the model training process. The project distinguishes itself through a comprehensive suite of tools for distributed training and model evaluation. It includes utilities for synchronizing gradients and coordinating collective communication across multiple GPUs or nodes, as well as a

This project is a comprehensive collection of practical code examples and implementation libraries for machine learning. It provides a wide array of reference materials for building supervised, unsupervised, and reinforcement learning algorithms. The repository serves as a multi-domain resource, featuring specific implementation suites for financial AI, Bayesian statistical modeling, and deep learning architectures. It includes a framework for training intelligent agents using policy gradients and actor-critic models, as well as practical guides for fine-tuning transformers and utilizing larg

This repository is a comprehensive collection of instructional guides and practical examples for Python development, focusing on machine learning, data science, and web scraping. It provides implementations for neural networks, reinforcement learning algorithms, and deep learning architectures using PyTorch, alongside detailed manuals for scientific computing and data visualization. The project distinguishes itself by offering specialized tutorials on concurrent programming to optimize CPU performance and guides for setting up Linux development environments. It covers the implementation of ad

CleanRL is a reinforcement learning library and PyTorch framework providing a suite of reproducible implementations for online reinforcement learning algorithms. It serves as a deep reinforcement learning benchmark suite and experiment orchestrator designed for research and agent development across both discrete and continuous action spaces. The project is distinguished by its single-file algorithm implementation approach, which encapsulates each algorithm in a standalone script to eliminate complex class hierarchies. This structure is paired with a system for scheduling and executing large-s

This is a PyTorch-based toolkit for training reinforcement learning agents, providing implementations of standard and hierarchical deep RL algorithms. It is designed as a library for deep reinforcement learning research and experimentation, supporting both discrete and continuous control tasks through a collection of algorithm implementations. The project distinguishes itself by offering a hierarchical reinforcement learning framework that decomposes complex long-horizon tasks into manageable sub-goals using meta-controllers and lower-level policies. It also includes a Hindsight Experience Re

Leela Zero is a deep learning Go engine and reinforcement learning system that implements the AlphaGo Zero approach. It utilizes deep residual convolutional networks and Monte Carlo Tree Search to determine optimal moves and analyze the game of Go. The project functions as a neural network training tool that generates data through automated self-play. It uses a supervised learning pipeline to refine network weights, allowing the system to improve its game-playing capabilities without relying on human-provided data or expert knowledge. The engine includes game scoring logic to determine winne

Horizon is a reinforcement learning platform designed for training, evaluating, and deploying agents and contextual bandits using historical data. It serves as an off-policy engine and offline policy evaluation tool, allowing decision-making policies to be optimized and tested without the need for a live simulator. The framework specializes in recommendation system optimization, specifically using slating-based reinforcement learning to optimize the ordering and sequencing of multiple recommendations. It also functions as a contextual bandit framework that manages the balance between explorat

Acme is a reinforcement learning framework and execution engine designed for developing and benchmarking learning algorithms. It provides a library of modular components and reference implementations used to construct agents and establish performance baselines. The system enables the scaling of agent architectures from single-stream execution to large distributed environments. This allows for the transition from initial prototyping to distributed execution for training and evaluation. The framework covers reinforcement learning development and agent architecture prototyping, providing the bu

Dopamine is a reinforcement learning research framework designed for prototyping and testing algorithms across diverse simulated environments. It provides an agent development toolkit that utilizes a flat class hierarchy to facilitate the creation and extension of learning agents. The framework includes a standardization layer via environment wrappers that connect agents to various physics simulations and gaming environments. It also features a high-performance experience replay buffer for storing and sampling transition data to improve training stability, alongside a dedicated hyperparameter

Trax is a deep learning framework and hardware-agnostic tensor engine designed for designing and training neural networks. It serves as a research tool providing high-level combinators for composing complex architectures, alongside a dedicated library for building transformer models and a toolkit for reinforcement learning. The framework is distinguished by its support for reversible and sparse transformer architectures, which reduce memory and computational overhead. It enables a single set of model instructions to execute across different hardware backends without changing the underlying co

Baselines is a comprehensive suite of frameworks for reinforcement learning algorithm implementation, imitation learning, and training orchestration. It provides a library of standardized learning algorithms used to benchmark and replicate research results, alongside a deep learning policy framework for constructing neural network architectures such as multi-layer perceptrons, convolutional networks, and long short-term memory networks. The project includes a specialized imitation learning toolkit that enables agents to mimic expert behavior through behavior cloning and generative adversarial

Keras is a high-level deep learning framework designed for constructing and training neural networks through the composition of modular, functional layers. It serves as a comprehensive modeling toolkit that provides standardized procedures for defining, evaluating, and deploying complex architectures. By utilizing a directed acyclic graph approach, the framework allows users to build intricate models with multiple inputs, outputs, and shared layers, ensuring consistent numerical execution through functional state management. The project distinguishes itself as a multi-backend machine learning

Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch

QKeras: a quantization deep learning library for Tensorflow Keras

Distributed Deep learning with Keras & Spark

A simplified framework and utilities for PyTorch

Accelerated deep learning R&D

Keras Hyperopt: A very simple wrapper for convenient hyperparameter optimization

Pyro is a deep probabilistic programming library and differentiable probabilistic modeler designed for Bayesian inference. It functions as a probabilistic programming language that allows for the construction of complex graphical models using PyTorch tensors and automatic differentiation. The framework enables the definition of universal probabilistic models as standard Python functions. It integrates deep learning with probabilistic modeling to compute posterior distributions and estimate latent variables through gradient-based optimization and algorithmic solvers. The system provides a pro

TensorLight - A high-level framework for TensorFlow

Wrapper library for text generation / language models at character and word level with RNNs in TensorFlow

Ludwig is a multimodal machine learning platform and low-code framework designed for building, training, and deploying neural networks. It enables the construction of models that process text, images, audio, and tabular data through a unified interface using declarative configuration files rather than custom code. The system features a specialized low-code framework for large language models, supporting supervised fine-tuning, preference alignment, and a constrained decoding tool to force structured data output via logit extraction. It also includes an automated model architecture search to i

GPyTorch is a GPU-accelerated probabilistic framework and PyTorch library for implementing scalable Gaussian process models. It provides a system for Gaussian process modeling and uncertainty estimation, designed to perform efficient matrix operations on graphics hardware. The framework features a modular kernel system for constructing custom covariance functions and modeling complex data dependencies. It specifically integrates Gaussian processes with deep neural networks to create hybrid models for regression and classification. The system employs numerical linear algebra techniques, inclu

Hyperparameter Experiments with TensorFlow and Keras