Projects sharing features with Google Deepmind Lab

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

PySC2 is a Python library that exposes StarCraft II as a reinforcement learning environment. Agents receive observations and send actions through repeated observe-and-act cycles, with one or two players in the same game session. It is built as a research platform for developing and benchmarking machine learning agents on a real-time strategy game with partial information and long-horizon planning. The way the game is presented to an agent is a central part of the project. Observations come as stacked feature layers for the screen and minimap, as rendered RGB images, or as raw unit data addres

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

FinRL is a reinforcement learning framework designed for the development, training, and backtesting of automated trading strategies. It functions as a quantitative finance toolkit that integrates deep learning algorithms with financial market simulations to address complex portfolio management and asset allocation tasks. The platform provides an end-to-end pipeline for transforming raw market data into actionable trading models. The project distinguishes itself through a layered, modular architecture that separates data processing, environment simulation, and agent training. This design allow

Unitree RL Gym is an integrated software framework designed for the simulation, training, and deployment of motion control policies for legged robotic platforms. It provides a comprehensive environment for developing locomotion strategies for quadruped and humanoid robots by leveraging reinforcement learning algorithms within high-fidelity physics-based simulations. The platform distinguishes itself by offering an end-to-end workflow that bridges the gap between virtual training and physical execution. It includes specialized tools for mapping raw sensor data into normalized state-space obser

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

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

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

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

RmlUi is a cross-platform UI renderer and middleware library that enables the creation of user interfaces using a subset of HTML and CSS. It functions as a rendering-agnostic layer designed to integrate web-standard layout and styling into custom game engines and embedded applications. The framework is distinguished by its integration of Lua for dynamic logic and control, as well as a specialized toolkit for rendering SVG images and Lottie animations. It utilizes a pluggable rendering backend that decouples geometry generation from the final display, allowing it to generate textured geometry

Panda3D is a cross-platform game engine and 3D graphics rendering system developed for Python and C++. It functions as a comprehensive framework for building interactive 3D applications, providing a real-time physics simulator and a specialized 3D asset pipeline tool. The engine distinguishes itself by combining a high-performance C++ core with interoperable Python language bindings. It utilizes a scene graph architecture to organize 3D objects and provides a pipeline-based asset conversion system to optimize models and textures for runtime loading. Its capability surface includes low-level

Steamtinkerlaunch is a game launch wrapper and Wine prefix orchestrator designed to manage the execution environment of games on Linux. It functions as a compatibility layer manager that downloads and organizes custom Wine and Proton versions, while creating isolated filesystem hierarchies and registry hives to prevent configuration conflicts between games. The project specializes in automating the installation of mod managers and script extenders within these isolated environments. It acts as a post-processing shader injector, managing the installation and dynamic library overrides required

This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi

Makie.jl is a high-performance Julia data visualization library and hardware-accelerated plotting engine used to create interactive 2D and 3D visualizations. It functions as a reactive visualization framework where plots update automatically via observables and compute graphs, and as a vector graphics generator for high-resolution academic output. The system is distinguished by its backend-agnostic rendering pipeline, which supports OpenGL, WebGL, and ray-traced scenes. It employs a grammar-of-graphics approach to map variables to aesthetic attributes and utilizes a hierarchical scene graph t

Mitsuba 2 is a physically based ray tracing engine and differentiable rendering framework designed to simulate realistic light transport and compute exact gradients of the rendering process with respect to scene parameters. The software functions as an optical simulation tool that models complex phenomena using monochromatic, RGB, or spectral color representations alongside optional polarization effects. The system incorporates an automatic differentiation engine that records mathematical operations during the rendering pass to solve inverse problems and optimize designs. A plugin-based scen

pysc2 is a Python interface and simulation framework that connects the StarCraft II game engine to machine learning agents. It acts as an API wrapper that exposes game internals as a set of observations and actions, providing a reinforcement learning environment for research and training. The framework includes tools for game replay analysis to extract data and sequences of actions from recorded matches for predictive modeling. It also provides an agent simulation environment to run and evaluate the performance of single or competing artificial intelligence agents. The system handles game ma

DRL-robot-navigation is a deep reinforcement learning platform and robotic simulation framework designed to train autonomous mobile robots for collision-free path planning. It uses neural network policies and physics-engine simulation environments to teach robots how to navigate toward target coordinates while avoiding obstacles. The software trains continuous control policies using twin delayed deep deterministic policy gradients over continuous state and action spaces. Training is guided by scalar reward signals derived from target proximity and obstacle avoidance distances. System compone

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

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

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

Open-AutoGLM is an autonomous agent framework designed to perform complex user workflows on mobile devices. By translating natural language instructions into precise sequences of taps, scrolls, and text inputs, the system enables the automation of mobile application interactions and testing. The platform distinguishes itself through a combination of vision-language processing and reinforcement learning. It converts graphical user interfaces into structured data, allowing agents to parse screen elements and map natural language commands to coordinate-based actions. To ensure reliability, the s

PyGame Learning Environment is a Python framework that provides a standardized interface for training artificial intelligence agents within diverse game environments. It functions as a communication layer that bridges reinforcement learning agents with game state observations, action inputs, and reward signals, allowing for consistent interaction across different game titles. The platform distinguishes itself by offering a headless execution mode that disables graphical rendering pipelines. By bypassing display overhead, this feature accelerates training cycles for automated agents during bac

This project is a collection of pretrained reinforcement learning agents and training scripts built on Stable Baselines3 and Gymnasium. It provides a framework for training agents to solve specific tasks, managing experiment reproducibility, and deploying pretrained models. The system includes a specialized benchmarking suite and optimization tools for tuning agent settings. It utilizes automated search spaces and distributed trials to maximize performance, while employing bootstrap sampling to generate statistically robust performance metrics and confidence intervals. Broad capabilities cov

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

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

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

IsaacGymEnvs is a GPU-accelerated physics sandbox and robotics policy training suite designed for reinforcement learning. It serves as a vectorized robotic simulator that runs thousands of parallel environments on GPUs to accelerate the training of neural networks. The project provides a sim-to-real transfer framework that utilizes domain randomization and physics variations to ensure policies trained in simulation are robust enough for deployment on real hardware. It distinguishes itself through a high-performance architecture that uses tensor-based state management to handle observations an