# Tutoring Reinforcement Learning via Feedback Control

arXiv: https://arxiv.org/abs/2012.06863 · HTML: https://mlsanity.com/paper/2012.06863/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [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)

We introduce a control-tutored reinforcement learning (CTRL) algorithm. The idea is to enhance tabular learning algorithms by means of a control strategy with limited knowledge of the system model. By tutoring the learning process, the learning rate can be substantially reduced. We use the classical problem of stabilizing an inverted pendulum as a benchmark to numerically illustrate the advantages and disadvantages of the approach.