Awesome GitHub RepositoriesSimulation Environments

Gym is a reinforcement learning environment toolkit and agent simulation framework. It provides a standardized API and a universal communication interface that defines how learning agents interact with simulation environments through actions and observations. The project includes a benchmark environment suite and a diverse library of pre-configured simulation worlds, including physics engines and classic control tasks. It enables the creation of custom simulation environments to train agents in specific operational scenarios while ensuring reproducibility across different learning algorithms.

Genesis is an embodied AI simulation platform and parallelized robotics simulator designed for training general-purpose robotic agents. It integrates a physics engine for robotics that calculates collisions and movements for rigid bodies, soft tissues, and fluids, alongside a photorealistic 3D rendering engine. The platform features a domain randomization framework to vary environment parameters across parallel simulations, aiding in sim-to-real transfer. It supports the integration of real-world captured light fields and Gaussian splatting to provide photorealistic backgrounds within simulat

Genesis World is an embodied AI simulation platform designed for training robotic agents through physics-based interactions. It centers on a multi-physics simulation engine that integrates rigid body, particle, and finite element method dynamics, supported by a parallel simulation kernel compiler that translates Python functions into optimized GPU and CPU kernels. The platform features a photorealistic robot renderer that utilizes path-tracing and Gaussian Splatting to generate synthetic training data. It includes a domain randomization framework to vary lighting and physical parameters acros

This project is a reinforcement learning toolkit and simulation-based AI trainer for creating intelligent agents within Unity simulations. It provides a multi-agent simulation framework for configuring cooperative or competitive scenarios and includes an environment wrapper that bridges simulations with standard machine learning libraries using gym-style interfaces. The system features a native cross-platform inference engine that executes trained neural network models for real-time decision making without external dependencies. It enables the acceleration of the learning process by running m

OpenMAIC is an LLM multi-agent education platform designed to create immersive, interactive classroom simulations. It functions as a learning environment where multiple AI agents collaborate through a state-machine orchestration framework to coordinate conversational turns and interactions. The platform features an AI-driven interactive lesson generator that transforms documents and topics into educational experiences including slides, quizzes, and project activities. It integrates a speech-enabled interface that combines speech-to-text and text-to-speech for voice-based interaction, alongsid

AirSim is a high-fidelity simulation platform designed for the development and testing of autonomous vehicles. Built as a plugin for game engines, it provides a physics-based environment that models vehicle dynamics and sensor data, serving as a foundation for robotics research, computer vision training, and reinforcement learning. The platform distinguishes itself through its support for hardware-in-the-loop and software-in-the-loop testing, allowing developers to validate control logic and firmware against real-world signals or concurrent processes. It offers extensive programmatic control

This project is an AI research implementation library and machine learning research repository. It provides a collection of reference code, illustrative implementations, and open-source research datasets used to verify hypotheses and build upon existing models in artificial intelligence. The repository focuses on scientific research reproduction by translating theoretical findings from published papers into executable code. It includes specialized scientific simulation environments designed to test the behavior of autonomous agents and models within controlled settings. The project covers AI

CARLA is an autonomous driving simulator and research environment designed for developing and validating self-driving software. It functions as an urban traffic simulator that generates realistic vehicle and pedestrian behavior and as a synthetic sensor data generator producing LiDAR, Radar, and camera data. The platform distinguishes itself through its deep integration with robotics frameworks, specifically providing native connectivity to ROS2 nodes for robotic control and data processing. It supports the training of driving models via imitation and reinforcement learning within a controlle

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

jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti

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

Warp is a Python framework that JIT-compiles Python functions into CUDA kernels for GPU-accelerated parallel computation, with built-in automatic differentiation and multi-framework array interoperability. At its core, it provides a GPU kernel compilation system that enables writing and executing custom GPU kernels directly from Python, while supporting automatic gradient computation through those kernels for integration with machine learning pipelines. The framework also includes tile-based cooperative computing, where thread blocks partition into tiles for shared-memory and tensor-core opera

PufferLib is a reinforcement learning framework built around high-speed environment simulation and automatic hyperparameter optimization. It is designed to accelerate the entire RL training pipeline by running simulations at near-native speed and enabling the training of tiny models to super-human performance within seconds. The framework achieves its speed through a single-process training loop that eliminates inter-process communication overhead, vectorized batched simulation for parallel environment execution, and compiled C extensions that offload performance-critical computations. It als

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

Webots este un simulator de roboți bazat pe fizică și un mediu de dezvoltare utilizat pentru modelarea, programarea și testarea comportamentului roboților într-o lume fizică 3D simulată. Servește drept instrument de prototipare virtuală pentru a verifica sistemele mecanice și electronice prin crearea de modele virtuale de roboți și logică de control. Platforma permite un flux de lucru complet de simulare robotică, inclusiv dezvoltarea de controllere pentru roboți și programarea comportamentelor agenților autonomi. Se concentrează pe modelarea sistemelor fizice pentru a reprezenta proprietățile mecanice ale hardware-ului și a simula interacțiunile din lumea reală. Capabilitățile sistemului acoperă dezvoltarea de controllere pentru roboți, programarea agenților autonomi și modelarea și simularea generală a sistemelor robotice.

SUMO este o suită de simulare a traficului microscopic concepută pentru a modela mișcarea vehiculelor individuale și a pietonilor în rețele rutiere urbane la scară largă. Acesta funcționează ca un simulator de transport multimodal care integrează mașini, pietoni, biciclete, căi ferate și căi navigabile într-un singur mediu, susținut de instrumente pentru generarea rețelelor rutiere și modelarea cererii de trafic. Proiectul se distinge prin seturi de instrumente specializate pentru analiza impactului asupra mediului, care calculează emisiile vehiculelor și consumul de energie pentru flotele electrice și hibride. Oferă capabilități cuprinzătoare pentru modelarea vehiculelor autonome și a sistemelor de conducere automatizată, precum și un generator de rețea rutieră care importă date spațiale și definiții XML standard în industrie. Sistemul acoperă o gamă largă de domenii operaționale, inclusiv gestionarea infrastructurii de trafic pentru programele semafoarelor, calibrarea fluxului de trafic folosind date reale de la senzori și generarea profilurilor de flux de vehicule prin matrice origine-destinație. De asemenea, suportă simularea incidentelor de trafic și crearea de dispozitive de detectare personalizate pentru a monitoriza sau influența fluxul de trafic. Software-ul utilizează fișiere XML structurate pentru definirea scenariilor și oferă o interfață externă pentru controlul simulării în timp real.

Acest proiect este un simulator de vehicule autonome conceput pentru a valida logica de conducere autonomă și a antrena algoritmi de deep learning într-un mediu virtual. Acesta funcționează ca o platformă de antrenare pentru dezvoltarea rețelelor neuronale care controlează mișcarea și direcția vehiculului pe baza datelor senzorilor vizuali. Simulatorul utilizează motorul de joc Unity pentru a oferi o lume bazată pe fizică unde algoritmii de conducere autonomă pot fi testați pe trasee rutiere virtuale fără utilizarea hardware-ului fizic. Sistemul integrează un backend de scripting C# cu un motor de fizică pentru detectarea coliziunilor și dinamica vehiculului. Utilizează simularea senzorilor bazată pe raycast pentru detectarea obstacolelor și comunicarea API bazată pe socket-uri pentru a transmite stările simulării și comenzile de control către modele externe de deep learning.

Drake is a robotics simulation framework and control system modeling tool used for designing, simulating, and verifying the dynamics of complex robotic systems. It functions as a multibody dynamics simulator and a mathematical optimization library, providing a suite of algorithms for trajectory optimization and the simulation of articulated robots. The framework is distinguished by its block-diagram system for composing dynamical subsystems and its ability to formulate and solve diverse mathematical programs, including linear, quadratic, and nonconvex nonlinear problems. It supports specializ

OpenSceneGraph git repository