Projects sharing features with Open Spiel

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 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

The Arcade Learning Environment is a research-focused platform that provides a high-performance emulation engine for Atari 2600 games. It serves as a standardized environment for training and evaluating artificial intelligence agents, bridging native game execution with machine learning libraries through a dedicated Python interface. The project distinguishes itself by offering robust support for parallel and vectorized environment execution, allowing multiple game instances to run simultaneously to accelerate data collection. It includes specialized tools for asynchronous command queuing and

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

DouZero is a deep reinforcement learning framework and training system designed to teach digital agents to master complex card games. It provides the infrastructure to implement high-throughput reinforcement learning pipelines and evaluate the competitive success of game agents. The system utilizes a distributed actor-learner architecture that separates game simulation actors from GPU training devices to accelerate model convergence. It combines Monte Carlo Tree Search with policy-based value estimation to determine optimal moves through recursive evaluation and random sampling. The toolkit

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 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

Grasscutter is a private game server emulator designed to replicate game backend logic and simulate core gameplay mechanics without the use of official servers. It functions as a system for simulating character progression, world entities, and general backend simulation to host a private instance of a specific anime game. The project includes a game object management console for spawning entities and controlling player inventories within the simulated environment. It also provides a server configuration tool featuring a guided interface to generate and manage the settings and configuration fi

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

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

Tensorlayer is a deep learning framework and cross-backend AI library used to construct and execute neural network models. It serves as a scientific neural network toolkit providing customizable layers and architectures designed for research applications in science and engineering. The library enables multi-backend model execution, allowing the same model code to run across different deep learning frameworks, GPUs, and specialized AI accelerators. It includes a reinforcement learning library that provides both low-level and high-level tools for developing intelligent agents.

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

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

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

TradeMaster is a reinforcement learning trading framework and algorithmic trading simulator designed for designing and testing quantitative trading strategies. The system provides a platform for developing reinforcement learning agents, managing quantitative portfolios, and optimizing trade execution using financial market data. The project features specialized components for multi-modality data preprocessing, a high-fidelity market environment simulation for strategy backtesting, and a quantitative portfolio manager for capital reallocation across multiple assets. It includes a trade executi

Osim-rl is a research environment designed for the development and evaluation of reinforcement learning agents within physics-based musculoskeletal simulations. It provides a standardized interface that maps physiological state observations to muscle excitation control signals, enabling the study of human movement and biomechanics through iterative policy optimization. The framework distinguishes itself by integrating high-fidelity musculoskeletal modeling with tools for scientific benchmarking and reproducible experimentation. It allows researchers to define custom reward functions and adjus

This project is a Python collection of algorithms and data structures that implement the concepts from the Artificial Intelligence: A Modern Approach textbook. It serves as an educational resource for learning core artificial intelligence concepts through the implementation of classic algorithms for searching, logic, and problem solving. The repository functions as an automated reasoning toolset for managing knowledge bases, a game theory engine for calculating optimal moves in competitive games, and a search and optimization library. It provides specialized frameworks for deriving logical co

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.

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

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

This is a collection of classical algorithms and data structures implemented as a header-only C++ library. It provides a suite of tools for general algorithm implementation, including data structure management, graph theory analysis, and string processing. The library is distinguished by its specialized toolkits for cryptographic hashing and encoding, featuring implementations of MD5, SHA-1, and Base64. It also includes advanced capabilities for high-performance string processing via suffix trees and arrays, as well as computational number theory for primality testing and arbitrary-precision

This repository serves as a comprehensive library for algorithmic problem solving, providing reference implementations for fundamental computer science challenges. It is designed as a resource for technical interview preparation and competitive programming training, focusing on the mastery of common patterns and data structures required for coding assessments. The project distinguishes itself by offering solutions that emphasize idiomatic Python usage and performance optimization. It covers a wide range of algorithmic techniques, including greedy selection, dynamic programming, graph theory,

This project is a comprehensive collection of C++ libraries and toolkits providing reference implementations for data structures, graph algorithms, and bitwise logic. It serves as a C++ algorithm reference containing over 180 solved coding problems and a specialized toolkit for competitive programming. The repository distinguishes itself through extensive low-level bit manipulation libraries for parity checks, endianness detection, and XOR-based logic. It also provides a wide array of reference solutions for complex algorithmic challenges involving backtracking, graph theory, and dynamic prog

RLcard is an open-source framework for developing and evaluating reinforcement learning agents across multiple card game environments. It functions as a card game environment simulator, a multi-agent RL platform, and a benchmarking toolkit for algorithms like DQN, NFSP, and CFR. The framework provides a game-agnostic environment interface that decouples agent logic from game mechanics, allowing any policy to interact through a common API. It supports pluggable reinforcement learning algorithms that operate on this interface without modifying game logic, and includes a self-play training loop

This project serves as an educational resource and training framework for developing intelligent agents through deep reinforcement learning. It provides a collection of practical tutorials and code examples designed to teach the implementation of neural networks for solving complex decision-making tasks. By focusing on hands-on learning, the material guides users through the process of building autonomous systems that improve their performance through trial and error. The framework centers on the integration of standardized simulation environments, allowing agents to interact with diverse tas

Gym-anytrading is a reinforcement learning toolkit designed to simulate financial market conditions for the development and evaluation of automated trading agents. It provides a standardized framework that models stock and forex market data, allowing researchers to train agents through trial and error within consistent, gym-compatible environments. The platform distinguishes itself through an object-oriented architecture that enables users to define custom trading logic, including unique reward functions, profit calculations, and trade fee policies. By transforming raw financial datasets into

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