Search Results for "multi agent simulation" - Page 12
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Showing 342 open source projects for "multi agent simulation"
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RLCard
Reinforcement Learning / AI Bots in Card (Poker) Games
RLCard is a toolkit for reinforcement learning research on card games. It includes several popular card games and focuses on learning algorithms for imperfect information games like poker and blackjack. -
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Google Research Football
Check out the new game server
Google Research Football is a reinforcement learning environment simulating soccer matches. It focuses on learning complex behaviors such as team collaboration and strategy formation in competitive settings. -
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DouZero
[ICML 2021] DouZero: Mastering DouDizhu
DouZero is a reinforcement learning-based AI for playing DouDizhu, a popular Chinese card game. It focuses on perfecting AI strategies for competitive play using value-based deep RL techniques. -
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galacteek
Multi-platform browser for the distributed web
galacteek is a multi-platform Qt5-based browser and semantic agent for the distributed web. Be sure to install all the gstreamer packages on your system to be able to use the mediaplayer. After opening/mounting the DMG image, hold Control and click on the galacteek icon, and select Open and accept. You probably need to allow the system to install applications from anywhere in the security settings. -
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Multi-Agent path planning in Python
Python implementation of a bunch of multi-robot path-planning
multi_agent_path_planning is a Python-based implementation of multi-agent pathfinding algorithms for coordinating multiple agents in shared environments without collisions. It is useful in robotics, warehouse automation, and gaming AI. -
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IOS13-SimulateTouch
iOS Automation Framework iOS Touch Simulation Library
A system-wide touch event simulation library for iOS 11.0 - 14. This library enables you to simulate touch events on iOS 11.0 - 14 with just one line of code! Currently, the repository is mainly for programmers. In the future, I will make it suitable for people who do not understand how to code. -
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Multi-Agent Emergence Environments
Environment generation code for the paper "Emergent Tool Use"
multi-agent-emergence-environments is an open source research environment framework developed by OpenAI for the study of emergent behaviors in multi-agent systems. It was designed for the experiments described in the paper and blog post “Emergent Tool Use from Multi-Agent Autocurricula”, which investigated how complex cooperative and competitive behaviors can evolve through self-play. -
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gym-pybullet-drones
PyBullet Gymnasium environments for multi-agent reinforcement
Gym-PyBullet-Drones is an open-source Gym-compatible environment for training and evaluating reinforcement learning agents on drone control and swarm robotics tasks. It leverages the PyBullet physics engine to simulate quadrotors and provides a platform for studying control, navigation, and coordination of single and multiple drones in 3D space. -
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The Development System for Auditory Modelling (DSAM) is a computational library designed specifically for producing simulations of the auditory system. It brings together many established auditory models within a flexible programming platform.
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phpsploit
Full-featured C2 framework which silently persists on webserver
...The obfuscated communication is accomplished using HTTP headers under standard client requests and web server's relative responses, tunneled through a tiny polymorphic backdoor. Detailed help for any option (help command) Cross-platform on both client and server. CLI supports auto-completion & multi-command. Session saving/loading feature & persistent history. Multi-request support for large payloads (such as uploads) Provides a powerful, highly configurable settings engine. Each setting, such as user-agent has a polymorphic mode. Customizable environment variables for plugin interaction. Provides a complete plugin development API. -
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MADDPG
Code for the MADDPG algorithm from a paper
MADDPG (Multi-Agent Deep Deterministic Policy Gradient) is the official code release from OpenAI’s paper Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. The repository implements a multi-agent reinforcement learning algorithm that extends DDPG to scenarios where multiple agents interact in shared environments. Each agent has its own policy, but training uses centralized critics conditioned on the observations and actions of all agents, enabling learning in cooperative, competitive, and mixed settings. ... -
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End-to-End Negotiator
Deal or No Deal? End-to-End Learning for Negotiation Dialogues
End-to-End Negotiator is a PyTorch-based research framework developed by Facebook AI Research to train neural agents capable of conducting strategic negotiations in natural language. The project implements the models presented in two key papers: “Deal or No Deal? End-to-End Learning for Negotiation Dialogues” and “Hierarchical Text Generation and Planning for Strategic Dialogue”. It enables agents to plan, reason, and communicate effectively to maximize outcomes in multi-turn negotiations... -
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SmartBody
Character animation system for games and simulations.
SmartBody is available for download for Windows, Linux and OSX users. SmartBody can also be used on Android and iOS platforms. SmartBody is a character animation platform that provides the following capabilities in real time: * Locomotion (walk, jog, run, turn, strafe, jump, etc.) * Steering - avoiding obstacles and moving objects * Object manipulation - reach, grasp, touch , pick up objects * Lip Syncing - characters can speak with simultaneous lip-sync using text-to-speech or... -
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SMAC
SMAC: The StarCraft Multi-Agent Challenge
SMAC (StarCraft II Multi-Agent Challenge) is a benchmark environment for cooperative multi-agent reinforcement learning (MARL), based on real-time strategy (RTS) game scenarios in StarCraft II. It allows researchers to test algorithms where multiple units (agents) must collaborate to win battles against built-in game AI opponents. SMAC provides a controlled testbed for studying decentralized execution and centralized training paradigms in MARL. -
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Neural MMO
Code for the paper "Neural MMO: A Massively Multiagent Game..."
Neural MMO is a massively multi-agent simulation environment developed by OpenAI for reinforcement learning research. It provides a persistent, procedurally generated world where thousands of agents can interact, compete, and cooperate in real time. The environment is inspired by Massively Multiplayer Online Role-Playing Games (MMORPGs), featuring resource gathering, combat mechanics, exploration, and survival challenges. -
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Coach
Enables easy experimentation with state of the art algorithms
Coach is a python framework that models the interaction between an agent and an environment in a modular way. With Coach, it is possible to model an agent by combining various building blocks, and training the agent on multiple environments. The available environments allow testing the agent in different fields such as robotics, autonomous driving, games and more. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms and allows simple integration of new environments... -
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ECommerceCrawlers
Collection of Python ecommerce and website crawler examples projects
ECommerceCrawlers is a collection of practical Python web crawler projects designed to gather data from a variety of ecommerce platforms, websites, and online services. It aggregates many independent crawler examples created by contributors and organized into separate subprojects that target specific sites or data sources. These examples demonstrate how to build and operate web scrapers capable of collecting structured information such as product listings, news content, job postings, social... -
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Virtual Laboratory Environment
A multi-modeling and simulation environment to study complex systems
VLE is a multi-modeling and simulation environment to study complex dynamic systems. VLE is based on the discrete event specification DEVS. and it implements the DSDE formalism (A merge of Dynamic Structure DEVS, DSDEVS, with Parallel DEVS, PDEVS). VLE provides a complete set of C++ libraries, called VFL (VLE Foundation Libraries), to develop DEVS models, to gets results of simulations, to launch simulation on cluster. -
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AESOP-ACP
A simulation framework for production process modeling in Python
ACP is a simulation framework aimed to production processes modeling. Compared to its ancestor - jES - it is lightweight, more general and written in Python. The basic goal of the simulator is to find bottlenecks in production process which could be hard to detect with traditional approaches (e.g., top-down). In addition, it can be used to speculate about “what-if” scenarios in order to suggest solution strategies. -
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Universe Starter Agent
A starter agent that can solve a number of universe environments
The universe-starter-agent repository is an archived OpenAI codebase designed as a starter reinforcement-learning agent that can interact with and solve tasks in OpenAI’s Universe environment platform. Its purpose is to serve as a baseline or reference implementation so researchers or developers can see how to build agents that operate in real-time, visual environments (e.g., games, browser apps) via pixel observations and keyboard/mouse actions. Under the hood, this starter agent implements... -
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Madara
Middleware for distributed applications
The purpose of the project is to develop a portable programming framework that facilitates distributed and multi-threaded programming for C++, Java, and Python. MADARA was originally developed as an agent-based middleware specifically for real-time, distributed artificial intelligence, but is now more general purpose for distributed timing, control, knowledge and reasoning, and quality-of-service. MADARA is composed of several tools and middleware, and the main entry point into the system is the Knowledge and Reasoning Language (KaRL) Engine, which provides a real-time scripting language for nanosecond execution times hooked into a flexible transport layer for distributed reasoning. ... -
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darc
Durham Adaptive-optics Real-time Controller
darc, the Durham Adaptive optics Real-time Controller. For documentation or darctalk client only, select "View all files". For the latest bleeding-edge version, please use: git clone git://git.code.sf.net/p/darc2/code darc (no password required) (this changed May 2013 due to a sourceforge update). If you use darc, please cite with: Basden, A and Myers, R, MNRAS Vol 242, page 1483, 2012 -
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House3D
A Realistic and Rich 3D Environment
House3D is a large-scale virtual 3D simulation environment designed to support research in embodied AI, reinforcement learning, and vision-language navigation. It provides more than 45,000 richly annotated indoor scenes sourced from the SUNCG dataset, covering diverse architectural layouts such as studios, multi-floor homes, and spaces with detailed furnishings and room types. -
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Acoustic Research Tool (ART)
Acoustic Simulation Library for Frequency and Time Domain Simulations.
ART is a flexible simulation framework for wind instruments. It includes a growing library of modelling elements. So far bore discontinuities, branches, tone holes, cylindrical and conical tubes, Bessel horns and bent tubes are available for frequency domain modelling. In the time domain generic bidirectional propagation elements, scattering elements, fractional delays, convolution with reflection functions and general z-domain networks are available and can be described using MuParserX... -
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MuPIF
Multi-Physics Integration Framework (MuPIF)
Multi-Physics Integration Framework (MuPIF) is an integration framework, that will facilitate the implementation of multi-physic and multi-level simulations, built from independently developed components. The principal role of the framework is to steer individual components (applications) and to provide high-level data-exchange services. Each application should implement an interface that allows to steer application and execute data requests. The design supports various coupling strategies,...
