Open-source alternatives to Pysc2

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

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

SerpentAI is a game AI development kit and computer vision framework designed for building autonomous agents that interact with video games. It serves as a game input automation tool and a machine learning model integration engine, allowing developers to create agents that perceive game states and execute actions. The framework utilizes a plugin-based agent architecture to provide modular extensions for game-specific logic and behaviors. It features a specialized system for training, bundling, and deploying machine learning classifiers to recognize visual contexts and game states in real time

This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva

Agentverse is a multi-agent framework and orchestrator designed for deploying and managing multiple large language model agents. It provides a simulation environment where agents interact based on custom personas and defined interaction rules to solve tasks or simulate social dynamics. The system features a tool integration layer that connects agents to external functional plugins and specialized tools, extending their capabilities beyond text generation. It uses a combination of persona-based prompt injection and state-managed memory to maintain agent consistency and specialized skill sets d

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 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 a .NET implementation of the gRPC framework, providing a system for executing functions on remote servers as if they were local calls. It serves as a high-performance remote procedure call (RPC) framework that utilizes HTTP/2 for service connectivity and binary communication protocols to ensure efficient data exchange. The implementation includes a gRPC-Web proxy, which acts as a translation layer to enable browser-based applications to communicate with gRPC services through web-compatible requests. It further supports the creation of HTTP/2 service meshes to connect distribut

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

Webots is a physics-based robot simulator and development environment used for modeling, programming, and testing the behavior of robots in a simulated 3D physical world. It serves as a virtual prototyping tool to verify mechanical and electronic systems through the creation of virtual robot models and control logic. The platform enables a full robotics simulation workflow, including the development of robot controllers and the programming of autonomous agent behaviors. It focuses on physical system modeling to represent the mechanical properties of hardware and simulate real-world interactio

s2client-proto provides a set of structured data definitions and language-neutral communication protocols used to exchange information between an external client and the StarCraft II engine. It utilizes protocol buffer definitions to establish a binary serialization schema for these data exchanges. The project defines the underlying communication layer necessary for game engine automation, game state analysis, and the development of software agents for competitive gaming. The framework covers interface definition languages to ensure compatibility across programming languages and employs sche

grpc-rust is a native gRPC framework for Rust designed for building high-performance remote procedure call clients and servers. It provides an asynchronous communication stack and a protocol buffers implementation for encoding, decoding, and generating type-safe code from service definitions. The project enables the implementation of unary and bi-directional data streaming over the HTTP/2 protocol. It includes specialized support for gRPC-Web integration, allowing browser-based clients to communicate with services through protocol translation. The infrastructure covers a broad range of distr

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 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

Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning

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

Massively parallel rigidbody physics simulation on accelerator hardware.

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 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-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

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

Oasis is an LLM-powered multi-agent social simulator and research tool designed to study synthetic social phenomena. It functions as a synthetic social network platform, replicating the infrastructure of social sites including user profiles, follow relationships, and content discovery mechanisms to model human-like social behaviors at scale. The framework orchestrates large-scale agent populations, supporting up to one million autonomous agents. It distinguishes itself by translating language model outputs into concrete social actions and external tool executions through a tool-calling orches

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

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

ManiSkill is a GPU-accelerated robot simulation framework designed for training robotic manipulation skills, benchmarking learning algorithms, and generating synthetic datasets. It serves as a reinforcement learning environment where robot control policies can be developed and evaluated using parallelized physics and rendering on the GPU. The platform is distinguished by its ability to perform sim-to-real transfer, allowing policies trained in virtual environments to be deployed onto physical robotic hardware. It features ray-traced parallel rendering for producing high-frame-rate RGBD and se

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

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,