ai-economist
Mava
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| ai-economist | Mava | |
|---|---|---|
| 5 | 5 | |
| 1,060 | 645 | |
| - | 5.7% | |
| 0.0 | 9.9 | |
| 8 months ago | 6 days ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ai-economist
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Agent-based modeling in applied economics?
3 Area of Reinforcement learning, in particular, has demonstrated impressive breakthroughs recently. There were attempts to apply it to economic policy planning and finance
- "The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning", Zheng et al 2021 {Salesforce}
- How to assemble the The AI Economist program in python ?
- IA economista comparou modelos de livre mercado, de taxação maior sobre os ricos, e seu próprio modelo de desenvolvimento para descobrir qual deles melhor promove alta produtividade e igualdade social. Resultado: livre mercado é o pior modelo, e a IA se saiu melhor que o modelo proposto por Saez
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Improving Equality and Productivity with AI-Driven Tax Policies
They're also on Github -> https://github.com/salesforce/ai-economist
Mava
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Starting wth Multi Agent Reinforcement Learning
If you want to play with models and algorithms around MARL, take a look at Mava.
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Recomendations of framework/library for MARL
These are the main reasons we built Mava , a library built specifically for MARL. We are also in the process of rewriting it to be simpler to compose MARL components (communication, central training etc), and we are rewriting our codebase in JAX, so really looking forward to improved performance! (Disclaimer, I am one of the people working on Mava).
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Training with multiple agents.
The OpenSpiel games (as well as several others) are also available from MAVA (https://github.com/instadeepai/Mava).
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[R] Mava: a research framework for distributed multi-agent reinforcement learning
https://arxiv.org/abs/2107.01460 Mava, a scalable framework for research in multi-agent reinforcement learning, contains implementation for several multi-agent systems like multi-agent DQN (MADQN), MADDPG, MAPPO, MAD4PG, DIAL, QMIX, and VDN, and integrates well with multi-agent RL environments like PettingZoo, Flatland, OpenSpiel, RoboCup, and SMAC. On GitHub: https://github.com/instadeepai/Mava
What are some alternatives?
pymarl2 - Fine-tuned MARL algorithms on SMAC (100% win rates on most scenarios)
acme - A library of reinforcement learning components and agents
robo-gym - An open source toolkit for Distributed Deep Reinforcement Learning on real and simulated robots.
lingvo - Lingvo
0xDeCA10B - Sharing Updatable Models (SUM) on Blockchain
tf2multiagentrl - Clean implementation of Multi-Agent Reinforcement Learning methods (MADDPG, MATD3, MASAC, MAD4PG) in TensorFlow 2.x
maro - Multi-Agent Resource Optimization (MARO) platform is an instance of Reinforcement Learning as a Service (RaaS) for real-world resource optimization problems.
MLflow - Open source platform for the machine learning lifecycle
multi_agent_path_planning - Python implementation of a bunch of multi-robot path-planning algorithms.
PettingZoo - An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities