The Effectiveness of World Models for Continual Reinforcement Learning
Abstract
World models power some of the most efficient reinforcement learning algorithms. In this work, we showcase that they can be harnessed for continual learning – a situation when the agent faces changing environments. World models typically employ a replay buffer for training, which can be naturally extended to continual learning. We systematically study how different selective experience replay methods affect performance, forgetting, and transfer. We also provide recommendations regarding various modeling options for using world models. The best set of choices is called Continual-Dreamer, it is task-agnostic and utilizes the world model for continual exploration. Continual-Dreamer is sample efficient and outperforms state-of-the-art task-agnostic continual reinforcement learning methods on Minigrid and Minihack benchmarks.
Cite
Text
Kessler et al. "The Effectiveness of World Models for Continual Reinforcement Learning." Proceedings of The 2nd Conference on Lifelong Learning Agents, 2023.Markdown
[Kessler et al. "The Effectiveness of World Models for Continual Reinforcement Learning." Proceedings of The 2nd Conference on Lifelong Learning Agents, 2023.](https://mlanthology.org/collas/2023/kessler2023collas-effectiveness/)BibTeX
@inproceedings{kessler2023collas-effectiveness,
title = {{The Effectiveness of World Models for Continual Reinforcement Learning}},
author = {Kessler, Samuel and Ostaszewski, Mateusz and Bortkiewicz, MichałPaweł and Żarski, Mateusz and Wolczyk, Maciej and Parker-Holder, Jack and Roberts, Stephen J. and Miłoś, Piotr},
booktitle = {Proceedings of The 2nd Conference on Lifelong Learning Agents},
year = {2023},
pages = {184-204},
volume = {232},
url = {https://mlanthology.org/collas/2023/kessler2023collas-effectiveness/}
}