MBMF: Model-Based Priors for Model-Free Reinforcement Learning

TL;DR

This paper introduces MBMF, a hybrid reinforcement learning method that combines model-based priors with model-free optimization to improve data efficiency and robustness, outperforming traditional approaches.

Contribution

The paper presents a novel approach that integrates probabilistic dynamics models as priors into model-free reinforcement learning, effectively bridging the two paradigms.

Findings

Outperforms purely model-based methods

Outperforms purely model-free methods

Better data efficiency and robustness

Abstract

Reinforcement Learning is divided in two main paradigms: model-free and model-based. Each of these two paradigms has strengths and limitations, and has been successfully applied to real world domains that are appropriate to its corresponding strengths. In this paper, we present a new approach aimed at bridging the gap between these two paradigms. We aim to take the best of the two paradigms and combine them in an approach that is at the same time data-efficient and cost-savvy. We do so by learning a probabilistic dynamics model and leveraging it as a prior for the intertwined model-free optimization. As a result, our approach can exploit the generality and structure of the dynamics model, but is also capable of ignoring its inevitable inaccuracies, by directly incorporating the evidence provided by the direct observation of the cost. Preliminary results demonstrate that our approach outperforms purely model-based and model-free approaches, as well as the approach of simply switching from a model-based to a model-free setting.