# Driving Reinforcement Learning with Models

arXiv: https://arxiv.org/abs/1911.04400 · HTML: https://mlsanity.com/paper/1911.04400/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2019.

## Built on by (most cited first, 3 of 3)

- [Reinforcement Learning with Adaptive Regularization for Safe Control of Critical Systems](https://mlsanity.com/paper/2404.15199/) (2024)
- [Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control](https://mlsanity.com/paper/2112.06018/) (2021)
- [CT-DQN: Control-Tutored Deep Reinforcement Learning](https://mlsanity.com/paper/2212.01343/) (2022)

## Abstract (arXiv)

In this paper we propose a new approach to complement reinforcement learning (RL) with model-based control (in particular, Model Predictive Control - MPC). We introduce an algorithm, the MPC augmented RL (MPRL) that combines RL and MPC in a novel way so that they can augment each other's strengths. We demonstrate the effectiveness of the MPRL by letting it play against the Atari game Pong. For this task, the results highlight how MPRL is able to outperform both RL and MPC when these are used individually.