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ai-economist-1.7.1
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Foundation: An Economics Simulation Framework
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | ai-economist-1.7.1 |
| نام | ai-economist |
| نسخه کتابخانه | 1.7.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Stephan Zheng, Alex Trott, Sunil Srinivasa |
| ایمیل نویسنده | stephan.zheng@salesforce.com |
| آدرس صفحه اصلی | https://github.com/salesforce/ai-economist |
| آدرس اینترنتی | https://pypi.org/project/ai-economist/ |
| مجوز | - |
# Foundation: An Economic Simulation Framework
This repo contains an implementation of Foundation, a framework for flexible, modular, and composable environments that **model socio-economic behaviors and dynamics in a society with both agents and governments**.
Foundation provides a [Gym](https://gym.openai.com/)-style API:
- `reset`: resets the environment's state and returns the observation.
- `step`: advances the environment by one timestep, and returns the tuple *(observation, reward, done, info)*.
This simulation can be used in conjunction with reinforcement learning to learn optimal economic policies, as detailed in the following papers:
**[The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies](https://arxiv.org/abs/2004.13332)**,
*Stephan Zheng, Alexander Trott, Sunil Srinivasa, Nikhil Naik, Melvin Gruesbeck, David C. Parkes, Richard Socher.*
**[The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning](https://arxiv.org/abs/2108.02755)**
*Stephan Zheng, Alexander Trott, Sunil Srinivasa, David C. Parkes, Richard Socher.*
**[Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist](https://arxiv.org/abs/2108.02904)**
*Alexander Trott, Sunil Srinivasa, Douwe van der Wal, Sebastien Haneuse, Stephan Zheng.*
If you use this code in your research, please cite us using this BibTeX entry:
```
@misc{2004.13332,
Author = {Stephan Zheng, Alexander Trott, Sunil Srinivasa, Nikhil Naik, Melvin Gruesbeck, David C. Parkes, Richard Socher},
Title = {The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies},
Year = {2020},
Eprint = {arXiv:2004.13332},
}
```
For more information and context, check out:
- [The AI Economist website](https://www.einstein.ai/the-ai-economist)
- [Blog: The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies](https://blog.einstein.ai/the-ai-economist/)
- [Blog: The AI Economist moonshot](https://blog.einstein.ai/the-ai-economist-moonshot/)
- [Blog: The AI Economist web demo of the COVID-19 case study](https://blog.einstein.ai/ai-economist-covid-case-study-ethics/)
- [Web demo: The AI Economist ethical review of AI policy design and COVID-19 case study](https://einstein.ai/the-ai-economist/ai-policy-foundation-and-covid-case-study)
## Simulation Cards: Ethics Review and Intended Use
Please see our [Simulation Card](https://github.com/salesforce/ai-economist/blob/master/Simulation_Card_Foundation_Economic_Simulation_Framework.pdf) for a review of the intended use and ethical review of our framework.
Please see our [COVID-19 Simulation Card](https://github.com/salesforce/ai-economist/blob/master/COVID-19_Simulation-Card.pdf) for a review of the ethical aspects of the pandemic simulation (and as fitted for COVID-19).
## Join us on Slack
If you're interested in extending this framework, discussing machine learning for economics, and collaborating on research project:
- join our Slack channel [aieconomist.slack.com](https://aieconomist.slack.com) using this [invite link](https://join.slack.com/t/aieconomist/shared_invite/zt-g71ajic7-XaMygwNIup~CCzaR1T0wgA), or
- email us @ ai.economist@salesforce.com.
## Installation Instructions
To get started, you'll need to have Python 3.7+ installed.
### Using pip
Simply use the Python package manager:
```python
pip install ai-economist
```
### Installing from Source
1. Clone this repository to your local machine:
```
git clone www.github.com/salesforce/ai-economist
```
2. Create a new conda environment (named "ai-economist" below - replace with anything else) and activate it
```pyfunctiontypecomment
conda create --name ai-economist python=3.7 --yes
conda activate ai-economist
```
3. Either
a) Edit the PYTHONPATH to include the ai-economist directory
```
export PYTHONPATH=<local path to ai-economist>:$PYTHONPATH
```
OR
b) Install as an editable Python package
```pyfunctiontypecomment
cd ai-economist
pip install -e .
```
Useful tip: for quick access, add the following to your ~/.bashrc or ~/.bash_profile:
```pyfunctiontypecomment
alias aiecon="conda activate ai-economist; cd <local path to ai-economist>"
```
You can then simply run `aiecon` once to activate the conda environment.
### Testing your Install
To test your installation, try running:
```
conda activate ai-economist
python -c "import ai_economist"
```
## Getting Started
To familiarize yourself with Foundation, check out the tutorials in the `tutorials` folder. You can run these notebooks interactively in your browser on Google Colab.
### Multi-Agent Simulations
- [economic_simulation_basic](https://www.github.com/salesforce/ai-economist/blob/master/tutorials/economic_simulation_basic.ipynb) ([Try this on Colab](http://colab.research.google.com/github/salesforce/ai-economist/blob/master/tutorials/economic_simulation_basic.ipynb)!): Shows how to interact with and visualize the simulation.
- [economic_simulation_advanced](https://www.github.com/salesforce/ai-economist/blob/master/tutorials/economic_simulation_advanced.ipynb) ([Try this on Colab](http://colab.research.google.com/github/salesforce/ai-economist/blob/master/tutorials/economic_simulation_advanced.ipynb)!): Explains how Foundation is built up using composable and flexible building blocks.
- [optimal_taxation_theory_and_simulation](https://github.com/salesforce/ai-economist/blob/master/tutorials/optimal_taxation_theory_and_simulation.ipynb) ([Try this on Colab](https://colab.research.google.com/github/salesforce/ai-economist/blob/master/tutorials/optimal_taxation_theory_and_simulation.ipynb)!): Demonstrates how economic simulations can be used to study the problem of optimal taxation.
- [covid19_and_economic_simulation](https://www.github.com/salesforce/ai-economist/blob/master/tutorials/covid19_and_economic_simulation.ipynb) ([Try this on Colab](http://colab.research.google.com/github/salesforce/ai-economist/blob/master/tutorials/covid19_and_economic_simulation.ipynb)!): Introduces a simulation on the COVID-19 pandemic and economy that can be used to study different health and economic policies .
### Multi-Agent Training
- [multi_agent_gpu_training_with_warp_drive](https://github.com/salesforce/ai-economist/blob/master/tutorials/multi_agent_gpu_training_with_warp_drive.ipynb) ([Try this on Colab](http://colab.research.google.com/github/salesforce/ai-economist/blob/master/tutorials/multi_agent_gpu_training_with_warp_drive.ipynb)!): Introduces our multi-agent reinforcement learning framework [WarpDrive](https://arxiv.org/abs/2108.13976), which we then use to train the COVID-19 and economic simulation.
- [multi_agent_training_with_rllib](https://github.com/salesforce/ai-economist/blob/master/tutorials/multi_agent_training_with_rllib.ipynb) ([Try this on Colab](http://colab.research.google.com/github/salesforce/ai-economist/blob/master/tutorials/multi_agent_training_with_rllib.ipynb)!): Shows how to perform distributed multi-agent reinforcement learning with [RLlib](https://docs.ray.io/en/latest/rllib/index.html).
- [two_level_curriculum_training_with_rllib](https://github.com/salesforce/ai-economist/blob/master/tutorials/two_level_curriculum_learning_with_rllib.md): Describes how to implement two-level curriculum training with [RLlib](https://docs.ray.io/en/latest/rllib/index.html).
To run these notebooks *locally*, you need [Jupyter](https://jupyter.org). See [https://jupyter.readthedocs.io/en/latest/install.html](https://jupyter.readthedocs.io/en/latest/install.html) for installation instructions and [(https://jupyter-notebook.readthedocs.io/en/stable/](https://jupyter-notebook.readthedocs.io/en/stable/) for examples of how to work with Jupyter.
## Structure of the Code
- The simulation is located in the `ai_economist/foundation` folder.
The code repository is organized into the following components:
| Component | Description |
| --- | --- |
| [base](https://www.github.com/salesforce/ai-economist/blob/master/ai_economist/foundation/base) | Contains base classes to can be extended to define Agents, Components and Scenarios. |
| [agents](https://www.github.com/salesforce/ai-economist/blob/master/ai_economist/foundation/agents) | Agents represent economic actors in the environment. Currently, we have mobile Agents (representing workers) and a social planner (representing a government). |
| [entities](https://www.github.com/salesforce/ai-economist/blob/master/ai_economist/foundation/entities) | Endogenous and exogenous components of the environment. Endogenous entities include labor, while exogenous entity includes landmarks (such as Water and Grass) and collectible Resources (such as Wood and Stone). |
| [components](https://www.github.com/salesforce/ai-economist/blob/master/ai_economist/foundation/components) | Components are used to add some particular dynamics to an environment. They also add action spaces that define how Agents can interact with the environment via the Component. |
| [scenarios](https://www.github.com/salesforce/ai-economist/blob/master/ai_economist/foundation/scenarios) | Scenarios compose Components to define the dynamics of the world. It also computes rewards and exposes states for visualization. |
- The datasets (including the real-world data on COVID-19) are located in the `ai_economist/datasets` folder.
## Releases and Contributing
- Please let us know if you encounter any bugs by filing a GitHub issue.
- We appreciate all your contributions. If you plan to contribute new Components, Scenarios Entities, or anything else, please see our [contribution guidelines](https://www.github.com/salesforce/ai-economist/blob/master/CONTRIBUTING.md).
## Changelog
For the complete release history, see [CHANGELOG.md](https://www.github.com/salesforce/ai-economist/blob/master/CHANGELOG.md).
## License
Foundation and the AI Economist are released under the [BSD-3 License](LICENSE.txt).
نیازمندی
| مقدار | نام |
|---|---|
| ==1.4.4 | appdirs |
| ==0.1.2 | appnope |
| ==20.1.0 | argon2-cffi |
| ==2.5.6 | astroid |
| ==1.10 | async-generator |
| ==21.2.0 | attrs |
| ==0.2.0 | backcall |
| ==4.9.3 | beautifulsoup4 |
| ==21.5b1 | black |
| ==3.3.0 | bleach |
| ==0.0.1 | bs4 |
| ==2020.12.5 | certifi |
| ==1.14.5 | cffi |
| ==4.0.0 | chardet |
| ==8.0.1 | click |
| ==0.10.0 | cycler |
| ==5.0.9 | decorator |
| ==0.7.1 | defusedxml |
| ==0.3 | entrypoints |
| ==1.1.0 | et-xmlfile |
| ==3.9.2 | flake8 |
| ==1.4.0 | GPUtil |
| ==2.10 | idna |
| ==1.1.1 | iniconfig |
| ==5.5.5 | ipykernel |
| ==7.31.1 | ipython |
| ==0.2.0 | ipython-genutils |
| ==7.6.3 | ipywidgets |
| ==5.8.0 | isort |
| ==0.18.0 | jedi |
| ==3.0.1 | Jinja2 |
| ==3.2.0 | jsonschema |
| ==1.0.0 | jupyter |
| ==6.1.12 | jupyter-client |
| ==6.4.0 | jupyter-console |
| ==4.7.1 | jupyter-core |
| ==0.1.2 | jupyterlab-pygments |
| ==1.0.0 | jupyterlab-widgets |
| ==1.3.1 | kiwisolver |
| ==1.6.0 | lazy-object-proxy |
| ==3.1.3 | lz4 |
| ==2.0.1 | MarkupSafe |
| ==3.2.1 | matplotlib |
| ==0.1.2 | matplotlib-inline |
| ==0.6.1 | mccabe |
| ==0.8.4 | mistune |
| ==0.4.3 | mypy-extensions |
| ==0.5.3 | nbclient |
| ==6.0.7 | nbconvert |
| ==5.1.3 | nbformat |
| ==1.5.1 | nest-asyncio |
| ==6.4.1 | notebook |
| ==1.21.0 | numpy |
| ==3.0.7 | openpyxl |
| ==20.9 | packaging |
| ==1.2.4 | pandas |
| ==1.4.3 | pandocfilters |
| ==0.8.2 | parso |
| ==0.8.1 | pathspec |
| ==4.8.0 | pexpect |
| ==0.7.5 | pickleshare |
| ==9.0.0 | Pillow |
| ==0.13.1 | pluggy |
| ==0.10.1 | prometheus-client |
| ==3.0.18 | prompt-toolkit |
| ==0.7.0 | ptyprocess |
| ==1.10.0 | py |
| ==2.7.0 | pycodestyle |
| ==2.20 | pycparser |
| ==3.10.1 | pycryptodome |
| ==2.3.1 | pyflakes |
| ==2.9.0 | Pygments |
| ==2.8.2 | pylint |
| ==2.4.7 | pyparsing |
| ==0.17.3 | pyrsistent |
| ==6.2.4 | pytest |
| ==2.8.1 | python-dateutil |
| ==2021.1 | pytz |
| ==5.4.1 | pyyaml |
| ==22.0.3 | pyzmq |
| ==5.1.0 | qtconsole |
| ==1.9.0 | QtPy |
| ==2021.4.4 | regex |
| ==2.25.1 | requests |
| ==1.6.3 | scipy |
| ==1.5.0 | Send2Trash |
| ==1.16.0 | six |
| ==2.2.1 | soupsieve |
| ==0.10.0 | terminado |
| ==0.5.0 | testpath |
| ==0.10.2 | toml |
| ==6.1 | tornado |
| ==4.60.0 | tqdm |
| ==5.0.5 | traitlets |
| ==3.10.0.0 | typing-extensions |
| ==1.26.5 | urllib3 |
| ==0.2.5 | wcwidth |
| ==0.5.1 | webencodings |
| ==3.5.1 | widgetsnbextension |
| ==1.12.1 | wrapt |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl ai-economist-1.7.1:
pip install ai-economist-1.7.1.whl
نصب پکیج tar.gz ai-economist-1.7.1:
pip install ai-economist-1.7.1.tar.gz