This is part of my course project for the course Foundations of Reinforcement Learning at ETH.
- Clone the repository
- Install the requirements
- Save the data inside the
data
folder and adjust the paths inmain.py
in order to load the correct.bag
files. - Run the
main.py
file
This will pre-process the data, then saves it as a .pkl
file (which can be loaded later) and then trains the model.
Pre-processing the data is a time-consuming process.
We investigate using offline reinforcement learning to enhance the navigation capabilities of legged robots using previously collected real-world data. Our approach utilizes a comprehensive dataset from past ANYmal missions to develop a helper component for navigation that predicts the likelihood of navigational events based on sensor inputs.
This project is part of the course project for the course Foundations of Reinforcement Learning at ETH.