# Solving the swing-up and balance task for the Acrobot and Pendubot with SAC

arXiv: https://arxiv.org/abs/2312.11311 · HTML: https://mlsanity.com/paper/2312.11311/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 3 of 3)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)

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

- [AI Olympics challenge with Evolutionary Soft Actor Critic](https://mlsanity.com/paper/2409.01104/) (2024)
- [Average-Reward Maximum Entropy Reinforcement Learning for Underactuated Double Pendulum Tasks](https://mlsanity.com/paper/2409.08938/) (2024)
- [Velocity-History-Based Soft Actor-Critic Tackling IROS'24 Competition "AI Olympics with RealAIGym"](https://mlsanity.com/paper/2410.20096/) (2024)

## Abstract (arXiv)

We present a solution of the swing-up and balance task for the pendubot and acrobot for the participation in the AI Olympics competition at IJCAI 2023. Our solution is based on the Soft Actor Crtic (SAC) reinforcement learning (RL) algorithm for training a policy for the swing-up and entering the region of attraction of a linear quadratic regulator(LQR) controller for stabilizing the double pendulum at the top position. Our controller achieves competitive scores in performance and robustness for both, pendubot and acrobot, problem scenarios.