Combined Reinforcement Learning via Abstract Representations

Authors

  • Vincent Francois-Lavet McGill University
  • Yoshua Bengio Universite de Montreal
  • Doina Precup McGill University
  • Joelle Pineau McGill Unversity

DOI:

https://doi.org/10.1609/aaai.v33i01.33013582

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.

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Published

2019-07-17

How to Cite

Francois-Lavet, V., Bengio, Y., Precup, D., & Pineau, J. (2019). Combined Reinforcement Learning via Abstract Representations. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 3582-3589. https://doi.org/10.1609/aaai.v33i01.33013582

Issue

Section

AAAI Technical Track: Machine Learning