Project obsolete
This repository contains the materials from the final project for the Deep Learning for Data Science (CIS522) course at the University of Pennsylvania. The project explores the use of imitation learning to train an agent to learn human gait in a more naturalistic manner than popular reinforcement learning agents.
data: This directory contains any relevant data files used in the project.
docs: This directory contains the final report and any other relevant documentation for the project.
TrainedAgents: This directory contains the output data and analysis results generated by the scripts in the "src" directory.
scripts: This directory contains the code for the models trained using ridge regression, SAC, TD3, and behavioral cloning.
src: This directory contains the source code for the project, including any utility scripts or libraries used.
tests: This directory contains scripts for testing the functionality and performance of the models.
The "scripts" folder holds the code for the models we trained: ridge regression, SAC, TD3, and behavioral cloning. These models were used to explore the feasibility of using imitation learning to train an agent to learn human gait in a more naturalistic manner than popular reinforcement learning agents.
The final report for the project can be found in the root directory. The report provides an overview of the project, including the research question, methodology, results, and discussion.
This project is licensed under the MIT License. See the LICENSE.md file for details.
To get started with using the code in this repository, clone the repository to your local machine and install any necessary dependencies. Then, use the scripts in the "scripts" directory to train and test the models on your own data.
Contributions to this project are welcome and encouraged. If you notice any bugs or have ideas for additional features, please submit a pull request or open an issue on the GitHub repository.