# Average-Reward Maximum Entropy Reinforcement Learning for Global Policy in Double Pendulum Tasks

arXiv: https://arxiv.org/abs/2505.07516 · HTML: https://mlsanity.com/paper/2505.07516/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 12 May 2025.

## 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, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [AI Olympics challenge with Evolutionary Soft Actor Critic](https://mlsanity.com/paper/2409.01104/) (2024)
- [Learning control of underactuated double pendulum with Model-Based Reinforcement Learning](https://mlsanity.com/paper/2409.05811/) (2024)
- [Reinforcement Learning for Robust Athletic Intelligence: Lessons from the 2nd 'AI Olympics with RealAIGym' Competition](https://mlsanity.com/paper/2503.15290/) (2025)
- [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)

This report presents our reinforcement learning-based approach for the swing-up and stabilisation tasks of the acrobot and pendubot, tailored specifcially to the updated guidelines of the 3rd AI Olympics at ICRA 2025. Building upon our previously developed Average-Reward Entropy Advantage Policy Optimization (AR-EAPO) algorithm, we refined our solution to effectively address the new competition scenarios and evaluation metrics. Extensive simulations validate that our controller robustly manages these revised tasks, demonstrating adaptability and effectiveness within the updated framework.