This work argues for an alternative approach to RL research, where we build on prior computational work, which we believe could significantly improve real-world RL adoption and help democratize it further.
Citing
To cite this paper, please use the following reference:
@article{agarwal2022beyond,
title={Beyond Tabula Rasa: Reincarnating Reinforcement Learning},
author={Agarwal, Rishabh and Schwarzer, Max and Castro, Pablo Samuel and Courville, Aaron and Bellemare, Marc G},
journal={arXiv preprint arXiv:2206.01626},
year={2022}
}
Authors
For questions, please contact us at: rishabhagarwal@google.com.