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        "description": "<h1 align=\"center\">\n  <a href=\"https://lpm.media.mit.edu/\" target=\"_blank\">\n    Large Population Models\n  </a>\n</h1>\n\n<p align=\"center\">\n  <strong>making complexity simple</strong><br>\n  differentiable learning over millions of autonomous agents\n</p>\n\n<p align=\"center\">\n  <a href=\"https://github.com/AgentTorch/AgentTorch/blob/master/license.md\" target=\"_blank\">\n    <img src=\"https://img.shields.io/badge/license-MIT-blue.svg\" alt=\"Released under the MIT license.\" />\n  </a>\n\n  <a href=\"https://agenttorch.github.io/AgentTorch/\" target=\"_blank\">\n    <img src=\"https://img.shields.io/badge/Quick%20Introduction-green\" alt=\"Documentation\" />\n  </a>\n  <a href=\"https://twitter.com/intent/follow?screen_name=ayushchopra96\" target=\"_blank\">\n    <img src=\"https://img.shields.io/twitter/follow/ayushchopra96?style=social&label=Get%20in%20Touch\" alt=\"Get in Touch\" />\n  </a>\n  <a href=\"https://join.slack.com/t/largepopulationmodels/shared_invite/zt-2jalzf9ki-n9nXG5FryVSMaPmEL7Wm2w\" target=\"_blank\">\n     <img src=\"https://img.shields.io/badge/Slack-4A154B?logo=slack&logoColor=white\" alt=\"Join Us\"/>\n  </a>\n</p>\n\nLarge Population Models (LPMs) help simulate million-size populations by\ndesigning realistic environments and capturing expressive indvidual behavior.\nOur goal is to \"re-invent the census\": built entirely in simulation, captured\npassively and used to protect country-scale populations. Our research is early\nbut actively making an impact - winning awards at AI conferences and being\ndeployed across the world. Learn more about LPMs\n[here](lpm.media.mit.edu/research.pdf).\n\nAgentTorch LPMs have four design principles:\n\n- **Scalability**: AgentTorch models can simulate country-size populations in\n  seconds on commodity hardware.\n- **Differentiability**: AgentTorch models can differentiate through simulations\n  with stochastic dynamics and conditional interventions, enabling\n  gradient-based learning.\n- **Composition**: AgentTorch models can compose with deep neural networks (eg:\n  LLMs), mechanistic simulators (eg: mitsuba) or other LPMs. This helps describe\n  agent behavior using LLMs, calibrate simulation parameters and specify\n  expressive interaction rules.\n- **Generalization**: AgentTorch helps simulate diverse ecosystems - humans in\n  geospatial worlds, cells in anatomical worlds, autonomous avatars in digital\n  worlds.\n\nAgentTorch is building the future of decision engines - inside the body, around\nus and beyond!\n\nhttps://github.com/AgentTorch/AgentTorch/assets/13482350/4c3f9fa9-8bce-4ddb-907c-3ee4d62e7148\n\n## Installation\n\n> AgentTorch is meant to be used in a Python 3.9 environment. If you have not\n> installed Python 3.9, please do so first from\n> [python.org/downloads](https://www.python.org/downloads/).\n\nInstall the framework using `pip`, like so:\n\n```sh\n> pip install git+https://github.com/agenttorch/agenttorch\n```\n\n> Some models require extra dependencies that have to be installed separately.\n> For more information regarding this, as well as the hardware the project has\n> been run on, please see [`docs/install.md`](docs/install.md).\n\n## Getting Started\n\nThe following section depicts the usage of existing models and population data\nto run simulations on your machine. It also acts as a showcase of the Agent\nTorch API.\n\nA Jupyter Notebook containing the below examples can be found\n[here](docs/tutorials/using-models/walkthrough.ipynb).\n\n### Executing a Simulation with Gradient Based Learning\n\n```py\n# re-use existing models and population data easily\nfrom agent_torch.models import covid\nfrom agent_torch.populations import astoria\n\n# use the executor to plug-n-play\nfrom agent_torch.core.executor import Executor\nfrom agent_torch.core.dataloader import LoadPopulation\n\n# agent_\"torch\" works seamlessly with the pytorch API\nfrom torch.optim import SGD\n\nloader = LoadPopulation(astoria)\nsimulation = Executor(model=covid, pop_loader=loader)\n\nsimulation.init(SGD)\nsimulation.execute()\n```\n\n## Guides and Tutorials\n\n### Understanding the Framework\n\nA detailed explanation of the architecture of the Agent Torch framework can be\nfound [here](docs/architecture.md).\n\n### Creating a Model\n\nA tutorial on how to create a simple predator-prey model can be found in the\n[`tutorials/`](docs/tutorials/) folder.\n\n### Contributing to Agent Torch\n\nThank you for your interest in contributing! You can contribute by reporting and\nfixing bugs in the framework or models, working on new features for the\nframework, creating new models, or by writing documentation for the project.\n\nTake a look at the [contributing guide](docs/contributing.md) for instructions\non how to setup your environment, make changes to the codebase, and contribute\nthem back to the project.\n\n## Impact\n\n> **AgentTorch models are being deployed across the globe.**\n\n![Impact](docs/media/impact.png)\n",
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