# AI Olympics challenge with Evolutionary Soft Actor Critic

arXiv: https://arxiv.org/abs/2409.01104 · HTML: https://mlsanity.com/paper/2409.01104/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 2 Sep 2024.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 4 of 4)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Model-Based Policy Search Using Monte Carlo Gradient Estimation with Real Systems Application](https://mlsanity.com/paper/2101.12115/) (2021)
- [Solving the swing-up and balance task for the Acrobot and Pendubot with SAC](https://mlsanity.com/paper/2312.11311/) (2023)

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

- [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 Global Policy in Double Pendulum Tasks](https://mlsanity.com/paper/2505.07516/) (2025)

## Abstract (arXiv)

In the following report, we describe the solution we propose for the AI Olympics competition held at IROS 2024. Our solution is based on a Model-free Deep Reinforcement Learning approach combined with an evolutionary strategy. We will briefly describe the algorithms that have been used and then provide details of the approach