This codebase implements the representation learning method from The Laplacian in RL: Learning Representations with Efficient Approximations..
The implementation includes (i) representation learning and (ii) using the learned represetations for reward shaping.
This codebase is a re-implementation and was not the one used for generating the experiment results in the paper. The experiment code only includes the grid-world environments but not the Mujoco control ones.
Please refer to run_full_experiments.sh
for running representation learning, reward shaping, and visualizing representations. plot_curves.py
is for plotting the learning curve comparisons between different shaped rewards.
The code works with Python>=3.6 and PyTorch>=1.0.
If you use this codebase for your research, please cite the paper:
@inproceedings{wu2019laplacian,
title={The Laplacian in RL: Learning Representations with Efficient Approximations},
author={Wu, Yifan and Tucker, George and Nachum, Ofir},
booktitle={International Conference on Learning Representations},
year={2019}
}
Visualize learned representations:
Compare learning curves: