Combined Reinforcement Learning via Abstract Representations

Understand

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.