Urban mobility simulations with Python3, RLlib (Deep Reinforcement Learning) and Mesa (Agent-based modeling)

Last update: May 15, 2022

Overview

Deep Reinforcement Learning for Smart Cities

Documentation

RLlib: https://docs.ray.io/en/master/rllib.html

Mesa: https://mesa.readthedocs.io/en/stable/

Installation

Clone repository and install dependencies.

git clone [email protected]:eescriba/smart-cities-drl.git
cd smart-cities-drl
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
pip install -e .

Training

Train environments in Jupyter notebooks with RLlib.

SmartCab

WasteNet

Simulations

Run and visualize environments with Mesa.

mesa runserver src/[env_name]

Owner

GitHub Repository

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Urban mobility simulations with Python3, RLlib (Deep Reinforcement Learning) and Mesa (Agent-based modeling)

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Deep Reinforcement Learning for Smart Cities

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Simulations

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Urban mobility simulations with Python3, RLlib (Deep Reinforcement Learning) and Mesa (Agent-based modeling)

Related tags

Overview

Deep Reinforcement Learning for Smart Cities

Documentation

Installation

Training

SmartCab

WasteNet

Simulations

Owner

Implementation of CVPR'2022:Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priors

PyTorch(Geometric) implementation of G^2GNN in "Imbalanced Graph Classification via Graph-of-Graph Neural Networks"

Official repository accompanying a CVPR 2022 paper EMOCA: Emotion Driven Monocular Face Capture And Animation. EMOCA takes a single image of a face as input and produces a 3D reconstruction. EMOCA sets the new standard on reconstructing highly emotional images in-the-wild

Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation

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