rl-lunerlander

Introduction

Reinforcement learning (RL) planning in construction involves training agents to make decisions about construction tasks, scheduling, resource allocation, and risk management to improve project efficiency and reduce costs.

Getting Started

1. Clone repo and then run this command in your terminal to install all dependencies conda env create --name RL_Luner --file=environment.yml
2. Open terminal and run streamlit run app.py in project root.
3. Streamlit will create a port and open that network URL to see the app. If are connecting to a remote server, you need to forward the port to open it locally.

Prerequisites

• Python 3.6 or higher
• pytorch------------> Deep learning general library.
• torchvision--------> Deep learning for computer vision.
• torchaudio---------> Deep learning for audio and signal.

Data

In Details

Folder Structure and files in detail

├──  data
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├──  models 
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├── data converter                          - converting .sim file to excel 
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├──  notebooks   
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├── src                                     - source code for ML model.
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├── app.py                                  - app for demoing RL model usage with streamlit.
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├── environment.yml                         - essential libraries to create env which use conda channels for installation.
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├──  requirements.txt                       - third-party libraries to install with pip.

Plans

Challenges