# Learning control of underactuated double pendulum with Model-Based Reinforcement Learning

arXiv: https://arxiv.org/abs/2409.05811 · HTML: https://mlsanity.com/paper/2409.05811/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods.
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 9 Sep 2024.

## Placed in

- Deep reinforcement learning methods / [RL theory and sample complexity](https://mlsanity.com/deeprl/rl-theory-and-sample-complexity/)

## Builds on (most cited first, 2 of 2)

- [Model-Based Policy Search Using Monte Carlo Gradient Estimation with Real Systems Application](https://mlsanity.com/paper/2101.12115/) (2021)
- [Learning Control from Raw Position Measurements](https://mlsanity.com/paper/2301.13183/) (2023)

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

- [Learning global control of underactuated systems with Model-Based Reinforcement Learning](https://mlsanity.com/paper/2504.06721/) (2025)
- [Average-Reward Maximum Entropy Reinforcement Learning for Global Policy in Double Pendulum Tasks](https://mlsanity.com/paper/2505.07516/) (2025)

## Abstract (arXiv)

This report describes our proposed solution for the second AI Olympics competition held at IROS 2024. Our solution is based on a recent Model-Based Reinforcement Learning algorithm named MC-PILCO. Besides briefly reviewing the algorithm, we discuss the most critical aspects of the MC-PILCO implementation in the tasks at hand.