@inproceedings{
h{\"o}ftmann2023backward,
title={Backward Learning for Goal-Conditioned Policies},
author={Marc H{\"o}ftmann and Jan Robine and Stefan Harmeling},
booktitle={NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning},
year={2023},
url={https://openreview.net/forum?id=zjfGRftMPw}
}
Can we learn policies in reinforcement learning without rewards?  Can we learn a policy just by trying to reach a goal state?  We answer these questions positively by proposing a multi-step procedure that first learns a world model that goes backward in time, secondly generates goal-reaching backward trajectories, thirdly improves those sequences using shortest path finding algorithms, and finally trains a neural network policy by imitation learning.  We evaluate our method on a deterministic maze environment where the observations are $64\times 64$ pixel bird's eye images and can show that it consistently reaches several goals.
Backward Learning for Goal-Conditioned Policies
Marc Höftmann Jan Robine Stefan Harmeling