{ "info": { "author": "Sever Topan", "author_email": "", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Intended Audience :: Developers", "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6" ], "description": "AdjSim Simulation Framework\n===========================\n\n|Build Status| |Coverage Status| |License: GPL v3|\n\n Designed and developed by Sever Topan\n\nNavigation\n----------\n\n1. `Feature Abstract <#Features>`__\n2. `Installation Instructions `__\n3. `Tutorial `__\n4. `Class Documentation `__\n\nFeatures\n--------\n\nEngine\n~~~~~~\n\nAt its core, AdjSim is an agent-based modelling engine. It allows users\nto define simulation environments through which agents interact through\nability casting and timestep iteration. The framework is targeted\ntowards agents that behave intelligently, for example a bacterium\nchasing down food. However, the framework is extremely flexible - from\nenabling physics simulation to defining an environment in which\n`Conway's Game of\nLife `__ plays\nout! AdjSim aims to be a foundational architecture on top of which\nreinforcement learning can be built.\n\nGraphical Simulation Representation\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nThe simulation can be viewed in real time as it unfolds, with graphics\nare rendered and animated using PyQt5. Below are four of the distinct\nexamples packadged with AdjSim, ranging from bacteria to moon system\nsimulation.\n\n+-------------------+------------------------+\n| |Bacteria Demo| | |Predator Prey Demo| |\n+===================+========================+\n| |GOL Demo| | |Jupiter Demo| |\n+-------------------+------------------------+\n\nPost Simulation Analysis Tools\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nAgent properties can be marked for tracking during simulation, allowing\nfor viewing the results of these values once the simulation completes.\nFor example, we can track the population of each different type of\nagent, or the efficacy of the agent's ability to meet its intelligence\nmodule-defined goals.\n\n\\| |QLearning Graph|\\ \\| |Predator Prey Graph| \\|\n\\|:-------------:\\|:-------------:\\|\n\nIntelligence Module\n~~~~~~~~~~~~~~~~~~~\n\nPerhaps the most computationally interesting aspect of AdjSim lies in\nits intelligence module. It allows agents to set goals (for example, the\ngoal of a bacterium may be to maximize its calories), and assess its\nactions in terms of its ability to meet its goals. This allows the\nagents to learn which actions are best used in a given situation.\nCurrently the intelligence module implements\n`Q-Learning `__, but more\nadvanced reinforcement learning techniques are coming soon!\n\n.. |Build Status| image:: https://travis-ci.org/SeverTopan/AdjSim.svg?branch=master\n :target: https://travis-ci.org/SeverTopan/AdjSim\n.. |Coverage Status| image:: https://coveralls.io/repos/github/SeverTopan/AdjSim/badge.svg?branch=master\n :target: https://coveralls.io/github/SeverTopan/AdjSim?branch=master\n.. |License: GPL v3| image:: https://img.shields.io/badge/License-GPL%20v3-blue.svg\n :target: https://www.gnu.org/licenses/gpl-3.0\n.. |Bacteria Demo| image:: https://raw.githubusercontent.com/SeverTopan/AdjSim/master/gallery/images/readme_bacteria.png\n.. |Predator Prey Demo| image:: https://raw.githubusercontent.com/SeverTopan/AdjSim/master/gallery/images/readme_predator_prey.png\n.. |GOL Demo| image:: https://raw.githubusercontent.com/SeverTopan/AdjSim/master/gallery/images/readme_game_of_life.png\n.. |Jupiter Demo| image:: https://raw.githubusercontent.com/SeverTopan/AdjSim/master/gallery/images/readme_jupiter_moon_system.png\n.. |QLearning Graph| image:: https://raw.githubusercontent.com/SeverTopan/AdjSim/master/gallery/images/readme_individual_learning.png\n.. |Predator Prey Graph| image:: https://raw.githubusercontent.com/SeverTopan/AdjSim/master/gallery/images/readme_predator_prey_population.png\n\n\n", "description_content_type": null, "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/SeverTopan/AdjSim", "keywords": "agent based modelling ABM reinforcement learning", "license": "GPL-3.0", "maintainer": "", "maintainer_email": "", "name": "adjsim", "package_url": "https://pypi.org/project/adjsim/", "platform": "", "project_url": "https://pypi.org/project/adjsim/", "project_urls": { 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