Interpretable Dynamics Models for Data-efficient Reinforcement Learning

By Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek. 2019

4 citations recorded by the source corpus.

In this paper, we present a Bayesian view on model-based reinforcement learning. We use expert knowledge to impose structure on the transition model and present an efficient learning scheme based on variational inference. This scheme is applied to a heteroskedastic and bimodal benchmark problem on which we compare our results to NFQ and show how our approach yields human-interpretable insight about the underlying dynamics while also increasing data-efficiency.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Cuntai Guan
2. Erico Tjoa

In Statistical and Nonlinear Physics

1. Cuntai Guan
2. Erico Tjoa

Citations in the corpus

• A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, background): “X + Also see [107]–[111] N.”