Pinned Repositories
climate-cooperation-competition
AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N. ai4climatecoop.org
warp-drive
Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU (JMLR 2022)
ai-economist
Foundation is a flexible, modular, and composable framework to model socio-economic behaviors and dynamics with both agents and governments. This framework can be used in conjunction with reinforcement learning to learn optimal economic policies, as done by the AI Economist (https://www.einstein.ai/the-ai-economist).
ai-for-economics-seminar.github.io
Automated-multires-spatial-prediction
Multiresolution spatial prediction code
ELF-1
An End-To-End, Lightweight and Flexible Platform for Game Research
ml-for-economic-policy.github.io
neural-fingerprinting
pytorch-generative-model-collections
Collection of generative models in Pytorch version.
ray
A fast and simple framework for building and running distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.
StephanZheng's Repositories
StephanZheng/neural-fingerprinting
StephanZheng/Automated-multires-spatial-prediction
Multiresolution spatial prediction code
StephanZheng/ai-economist
Foundation is a flexible, modular, and composable framework to model socio-economic behaviors and dynamics with both agents and governments. This framework can be used in conjunction with reinforcement learning to learn optimal economic policies, as done by the AI Economist (https://www.einstein.ai/the-ai-economist).
StephanZheng/ai-for-economics-seminar.github.io
StephanZheng/ELF-1
An End-To-End, Lightweight and Flexible Platform for Game Research
StephanZheng/ml-for-economic-policy.github.io
StephanZheng/pytorch-generative-model-collections
Collection of generative models in Pytorch version.
StephanZheng/ray
A fast and simple framework for building and running distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.