Combined Reinforcement Learning via Abstract Representations

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

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.

Published
2019-07-17
Section
AAAI Technical Track: Machine Learning