Open-source alternatives to Lab

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

Universe is a training and evaluation platform that transforms websites, games, and software into standardized environments for general intelligence agents. It functions as a reinforcement learning wrapper and remote environment orchestrator, providing a consistent interface to wrap diverse software for AI agent interaction. The platform distinguishes itself through a visual observation interface that streams real-time pixel data and transmits keyboard and mouse events to simulate human interaction. It utilizes a bi-directional communication protocol to deliver reward signals and performance

Gymnasium is a suite of standardized APIs and simulation toolkits used to evaluate agent behavior and benchmark reinforcement learning algorithms. It provides a standardized interface for creating and interacting with simulated environments, enabling the training of reinforcement learning agents through a consistent set of interaction protocols. The project emphasizes experimental reproducibility through a versioned API and a system for tracking changes to environment logic using version suffixes. This ensures that learning results remain consistent and can be replicated across different soft

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

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

AI Town is a TypeScript-based simulation engine used to create virtual environments where autonomous characters interact and socialize. It functions as a framework for orchestrating multiple AI agents within a persistent digital world, utilizing language models and a game engine to drive character behavior and social interactions. The project differentiates itself through a dedicated agent sandbox and a vector database agent store, which allow for the management of agent memories and world state. It integrates generative AI for background music and provides tools for simulation world design,

PyBoy is a programmable Game Boy emulator and hardware simulation framework written in Python. It functions as an emulation engine that allows users to execute original handheld software while providing a programmatic interface to control, probe, and automate game execution. The project is specifically designed as a reinforcement learning environment, exposing emulator states and controls to facilitate the training of machine learning agents. It distinguishes itself by providing tools for game area mapping and the extraction of simplified 2D screen representations and collision maps to suppor

This project is a Python-based educational framework designed to simulate reinforcement learning algorithms and environments. It serves as a platform for reproducing classic textbook examples, allowing users to study agent behavior, policy improvement, and the fundamental mechanics of decision-making in controlled settings. The library provides implementations for core reinforcement learning concepts, including temporal difference learning, Monte Carlo episode sampling, and tabular value function approximation. It enables the analysis of specific algorithmic behaviors, such as identifying and

Open Spiel is a research library and framework for reinforcement learning, planning, and multi-agent game simulation. It provides a system for representing single-agent and multi-agent games across zero-sum, cooperative, and imperfect information scenarios. The project utilizes a standardized abstract game interface to decouple game logic from algorithms, allowing agents to run across different game types. It implements performance-critical logic in C++ with Python bindings and uses deterministic seeded simulation to ensure reproducibility for research benchmarking. The framework covers a br

Video-Pre-Training is a machine learning framework designed for training autonomous agents to perform complex tasks by observing and mimicking human behavior from video recordings. It provides a comprehensive toolkit for imitation learning and reinforcement learning research, enabling the development of agents that can replicate human actions within simulated digital environments. The framework distinguishes itself through its ability to process large-scale, unlabeled video datasets to bootstrap agent capabilities. It utilizes inverse dynamics modeling to infer control inputs from frame trans

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

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 project is an educational repository of reinforcement learning agents and tutorials implemented using TensorFlow. It provides a practical codebase for both model-free and model-based learning agents, designed to demonstrate how AI agents learn through trial and error. The collection features detailed implementations of various algorithmic approaches, including Deep Q-Networks and Policy Gradient methods. It specifically covers Actor-Critic architectures for continuous and discrete action spaces, alongside Proximal Policy Optimization and Deep Deterministic Policy Gradients. The framewor

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

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

Malmo is a voxel-based simulation platform designed for artificial intelligence research and the study of autonomous agent behaviors. Built as a sandbox environment using Minecraft, it serves as a framework for multi-agent simulation and reinforcement learning research within a 3D grid of blocks. The project distinguishes itself through a multi-agent simulation framework that coordinates and synchronizes multiple autonomous agents to perform collaborative missions. It provides a standardized interface following reinforcement learning specifications, allowing it to function as an environment f

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

This project is a reinforcement learning framework and game AI engine designed for training adversarial agents in two-player turn-based games. It implements a training loop that utilizes self-play and Monte Carlo Tree Search to produce neural networks capable of predicting board strength and move probabilities. The system decouples the reinforcement learning engine from specific game rules through an abstract game logic interface, allowing for the definition of custom game rules, win conditions, and board representations. It supports integration with various deep learning frameworks to serve

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

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

MuJoCo is a physics engine which can do very detailed efficient simulations with contacts. This library lets you use MuJoCo from Python.

Status: Archive (code is provided as-is, no updates expected)

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

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

TensorFlow Reinforcement Learning

Massively parallel rigidbody physics simulation on accelerator hardware.