Open-source alternatives to Tensorforce

TensorLayer is a backend-agnostic tensor library and deep learning framework designed for building neural network architectures. It provides a neural network abstraction layer that allows model logic to run across different deep learning engines using high-level layers and model components. The project serves as a deep reinforcement learning toolkit for implementing policy-based, value-based, and actor-critic agents. It includes specialized tools for managing experience replay and gradient-based policy optimization to handle both discrete and continuous action spaces. To support reinforcemen

keras-rl is a reinforcement learning library that enables the training of neural agents using Keras. It serves as a framework for implementing deep reinforcement learning agents that interact with simulated environments to discover optimal behaviors and maximize cumulative rewards. The library provides a system for configuring, training, and managing neural network agents. It handles the interaction loop between agents and environments, allowing models to learn through direct experience and gradient-based optimization. The framework includes capabilities for model weight management, allowing

OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.

This repository serves as a centralized collection of state-of-the-art deep learning architectures and reference implementations designed for research and application development. It provides a comprehensive toolkit for computer vision and natural language processing, offering pre-built models and training pipelines for tasks ranging from image classification and object detection to complex sequence modeling. The project distinguishes itself by providing a flexible execution harness that manages the entire training lifecycle, including data ingestion and backpropagation. It supports scalable

Lab is a customizable 3D platform and research testbed designed for training and testing autonomous agents using reinforcement learning. It serves as a spatial AI training simulator where agents can be evaluated through navigation and puzzle-solving tasks. The environment allows for the definition of complex layouts and task behaviors through external scripting, enabling the generation of specific challenges for AI research. It supports both automated training via standard API bindings and manual agent control to validate simulation dynamics. The system utilizes a grid-based spatial represen

Keras-js is a JavaScript inference engine and browser-based machine learning framework designed to execute pre-trained Keras neural networks. It allows for client-side model inference in web browsers or Node.js environments without the requirement of a backend server. The library utilizes a WebGL tensor accelerator to map mathematical operations to the graphics processor for hardware acceleration. To maintain user interface responsiveness during heavy computations, it incorporates a web worker inference runtime that executes neural network processing in background threads. The system support

Deep Reinforcement Learning for Keras.

SerpentAI is a game AI development kit and computer vision framework designed for building autonomous agents that interact with video games. It serves as a game input automation tool and a machine learning model integration engine, allowing developers to create agents that perceive game states and execute actions. The framework utilizes a plugin-based agent architecture to provide modular extensions for game-specific logic and behaviors. It features a specialized system for training, bundling, and deploying machine learning classifiers to recognize visual contexts and game states in real time

tflearn is a deep learning framework and high-level API wrapper for TensorFlow. It provides a toolkit for designing neural network architectures and a system for executing training loops and optimizing model weights across CPUs and GPUs. The project simplifies the process of building and training models through a modular interface and a high-level API for prototyping. It includes specialized utilities for deep learning visualization, allowing for the generation of graphical diagrams to analyze network structures, weights, gradients, and activations. The framework covers a broad range of capa

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

Maze Applied Reinforcement Learning Framework

Modular Deep Reinforcement Learning framework in PyTorch. Companion library of the book "Foundations of Deep Reinforcement Learning".

Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE

This repository contains the implementation of DISCERN in Python. You can download the manuscript from my website or arXiv.

Build fully-functioning computer vision models with PyTorch

DAMO-ConvAI: The official repository which contains the codebase for Alibaba DAMO Conversational AI.

Reinforcement Learning in PyTorch

Reinforcement Learning in Go

Hyperparameter Experiments with TensorFlow and Keras

You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery

A set of Deep Reinforcement Learning Agents implemented in Tensorflow.

This repository serves as a structured educational resource for machine learning and deep learning, providing a library of executable scripts and notebooks. It is designed to help users master the practical application of data processing, model evaluation, and neural network construction through annotated code samples and guided tutorials. The collection focuses on translating theoretical mathematical concepts into functional code, offering proven patterns for common tasks such as classification and regression. By providing curated examples of layer construction and training loops, the reposi

ROLL is a distributed reinforcement learning framework and model alignment toolkit designed for large language models. It serves as a scalable training pipeline and GPU cluster manager, providing the infrastructure to align model behavior using reinforcement learning algorithms and preference optimization techniques. The project distinguishes itself through an agentic rollout orchestrator that generates and collects multi-turn interaction trajectories between AI agents and simulated environments. It supports specialized alignment methods including Direct Preference Optimization, reinforcement