Combined Reinforcement Learning via Abstract Representations
Abstract
In the quest for efficient and robust reinforcement learning methods, both model-free and model-based approaches offer advantages. In this paper we propose a new way of explicitly bridging both approaches via a shared low-dimensional learned encoding of the environment, meant to capture summarizing abstractions. We show that the modularity brought by this approach leads to good generalization while being computationally efficient, with planning happening in a smaller latent state space. In addition, this approach recovers a sufficient low-dimensional representation of the environment, which opens up new strategies for interpretable AI, exploration and transfer learning.
Cite
@article{arxiv.1809.04506,
title = {Combined Reinforcement Learning via Abstract Representations},
author = {Vincent François-Lavet and Yoshua Bengio and Doina Precup and Joelle Pineau},
journal= {arXiv preprint arXiv:1809.04506},
year = {2018}
}
Comments
Accepted to the Thirty-Third AAAI Conference On Artificial Intelligence, 2019