Projects sharing features with Horizon

ReAgent is a reinforcement learning platform designed for training, deploying, and evaluating reinforcement learning models and contextual bandit systems for large-scale decision making. It provides a comprehensive suite of tools that spans the entire workflow from initial feasibility analysis to production serving. The system includes a deep reinforcement learning training framework for distributed off-policy algorithms and a specialized model serving layer for high-volume production inference. It distinguishes itself with a counterfactual policy evaluator for estimating performance using hi

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

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

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

Isaac Lab is an open-source framework for training robot policies in physically simulated environments, supporting both single-agent and multi-agent reinforcement learning. It is built on an Omniverse-PhysX simulation backend that models rigid bodies, articulated systems, deformable objects, and sensors, and provides a task-based environment configuration system where each training environment is defined as a modular class specifying observation spaces, action spaces, reward functions, and termination conditions. The framework distinguishes itself through an RL-library abstraction layer that

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

This repository provides a comprehensive library of reinforcement learning algorithms designed for training autonomous agents. It serves as a research-oriented collection of implementations that cover fundamental decision-making strategies, including dynamic programming, temporal difference learning, and policy gradient methods. The project distinguishes itself by offering specialized frameworks for deep reinforcement learning and structured decision modeling. It includes implementations for deep Q-learning that utilize neural networks, experience replay, and prioritized sampling to approxima

Vowpal Wabbit is an open-source machine learning system designed for online learning, where models update incrementally from streaming data without requiring full retraining. It provides a reduction-based learning framework that composes complex tasks from simpler algorithms, and includes a feature hashing trick that maps unbounded feature names into a fixed-size vector space to keep memory usage constant regardless of dataset size. The system supports distributed training across a cluster using an allreduce protocol for synchronized updates, and offers an active learning query strategy that s

Minigo is a TensorFlow-based reinforcement learning engine designed to master the game of Go. It functions as a comprehensive system for training neural networks to predict board policies and game outcomes, utilizing a model trainer to generate self-play data and optimize weights. The project is distinguished by its ability to perform large-scale game simulations using Kubernetes to distribute worker nodes across CPU, GPU, and TPU hardware. It employs a Monte Carlo Tree Search implementation to identify optimal moves and supports specialized hardware acceleration, including inference on Edge

rllm is an asynchronous reinforcement learning framework for training language agents. It provides a unified pipeline that runs the same agent code for both evaluation and training, automatically capturing traces for gradient computation. The framework supports distributed reinforcement learning across multiple GPUs and nodes using pluggable backends, and executes agents in isolated sandboxes—either locally or in the cloud—for safe and scalable rollout collection. It trains agents built with LangGraph, SmolAgents, OpenAI Agents SDK, or custom frameworks without requiring core logic changes. T

This project is a game AI training framework designed to develop and monitor reinforcement learning agents within a legacy game environment. It functions as a training and monitoring system that optimizes autonomous agents to complete game objectives through exploration and reward-based learning. The framework includes tools for game memory mapping and real-time trajectory visualization. These capabilities translate raw game memory addresses into visual coordinates, allowing agent movements and session data to be streamed to a map for the analysis of navigation patterns and area exploration.

This project is a comprehensive deep reinforcement learning course and training platform. It provides a structured educational curriculum that combines theoretical lessons with hands-on tutorials to teach the implementation of neural networks and agent behavior. The platform integrates a model sharing hub where users can upload, download, and version trained machine learning models. It also features a benchmarking system that uses leaderboards to evaluate and compare agent performance against community standards. The educational experience is delivered through interactive notebooks and inclu

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

This is a PyTorch reinforcement learning library designed for training agents in simulation environments. It provides a collection of deep reinforcement learning algorithms focusing on policy gradient methods and trust-region optimization. The library implements a suite of policy gradient algorithms, including A2C and PPO, alongside a framework for imitation learning using Generative Adversarial Imitation Learning. It specifically features a scalable implementation of the ACKTR algorithm, utilizing Kronecker-factored approximations to enable efficient trust-region optimization. The codebase

This repository is a collection of practical deep learning implementations and examples built using the TensorFlow framework. It provides a variety of neural network architectures focusing on natural language processing, recommendation systems, reinforcement learning, and time series prediction. The project features a range of specialized models, including sequence-to-sequence and transformer architectures for text processing, and factorization machines for personalized ranking and retrieval. It also includes implementations of reinforcement learning agents using actor-critic and policy gradi

LLM-RL-Visualized is a visual reference library and collection of knowledge maps designed to explain Large Language Model and Reinforcement Learning algorithms. It provides a structured system of conceptual diagrams and taxonomies covering the intersection of language model alignment and reinforcement learning. The project distinguishes itself through detailed visual mappings of complex workflows, such as the coordination of reward models and policy optimization in reinforcement learning from human feedback. It contrasts different preference optimization architectures, such as RLHF and Direct

This project is a PyTorch reinforcement learning library and agent training framework. It provides a suite of deep reinforcement learning algorithms, including DQN, PPO, and SAC, to facilitate the development of autonomous agents that optimize behavior through trial and error. The library focuses on the implementation of various actor-critic methods and deep learning architectures for research into autonomous decision making. It enables the training of intelligent agents within diverse environments by leveraging PyTorch-based model implementations. The codebase covers core reinforcement lear

Tensorpack is a high-level TensorFlow neural network framework and research library designed for building and training deep learning models. It provides a collection of reproducible neural network architectures for computer vision, generative tasks, reinforcement learning, and natural language processing. The project distinguishes itself through a specialized deep learning data pipeline that uses pure Python for parallel data loading and streaming. It includes a multi-GPU training orchestrator for distributing workloads via data-parallel strategies and a dedicated interpretability toolkit for

This project is a deep reinforcement learning curriculum providing educational materials and implementation exercises for mastering neural network-based agents. It serves as a framework for building reference versions of value-based and policy-based methods to solve sequential decision problems. The project provides specific implementations for continuous control simulations and multi-agent reinforcement learning, where agents are trained to cooperate or compete in shared environments. It includes a policy gradient framework for optimizing agent behavior through methods such as REINFORCE. Ca

RLinf is a distributed reinforcement learning orchestrator and embodied AI training framework. It provides the infrastructure to train vision-language-action models and robotic policies using a combination of reinforcement learning and supervised fine-tuning. The system is designed for scaling workloads across GPU clusters, managing the placement of actors, rollout workers, and environment components. It features a specialized robotics data collection pipeline for gathering teleoperated demonstrations and simulation trajectories into standardized replay buffers, alongside a hardware interface

Tianshou is a reinforcement learning framework designed for developing and testing agents. It provides a system for implementing custom agents by defining policies and parameter update rules to optimize agent behavior. The framework decouples neural network architectures from update logic through policy-based abstractions and separates data pre-processing from gradient updates. It utilizes a collector-driven pipeline to stream experience from environments into structured memory buffers for sampled learning. The system supports vectorized environment execution to run multiple parallel instanc

Stable-baselines3 is a reinforcement learning library built on the PyTorch deep learning framework. It provides a collection of reliable, standardized implementations of reinforcement learning algorithms designed for training, testing, and benchmarking agent policies in diverse simulated environments. The library functions as an agent training toolkit that emphasizes modularity and reproducibility. It features a unified environment interface and supports vectorized execution to accelerate data collection across multiple simulation instances. Users can customize neural network architectures, f

This project is an educational resource designed to teach the mathematical foundations and core algorithms of reinforcement learning. It provides a structured academic curriculum that combines textbooks, lecture materials, and practical code examples to guide learners through the principles of Markov decision processes and reinforcement learning theory. The repository distinguishes itself by integrating a grid-based simulation framework that allows users to test algorithms within custom environments. This environment supports the analysis of agent performance by rendering state values, polici

This repository is a collection of Jupyter notebooks providing reference implementations and templates for building, training, and deploying machine learning models using Amazon SageMaker. It serves as an example library for implementing model architectures and automating the machine learning lifecycle. The library provides practical patterns for machine learning training, data engineering, and model deployment. It includes implementation guides for MLOps, including workflows for model monitoring, lineage tracking, and hyperparameter tuning. The examples cover a broad range of capabilities i

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

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

OpenRLHF is a training framework and alignment library designed for reinforcement learning from human feedback across distributed GPU clusters. It provides tools for aligning large language models and multimodal vision-language models using algorithms such as PPO, GRPO, and DPO. The framework distinguishes itself through a distributed inference engine that overlaps sample rollout with training to increase throughput. It supports scaling to models exceeding 70 billion parameters via parameter sharding and handles long-context sequences through ring-attention sequence parallelism. The project