# Fast and stable MAP-Elites in noisy domains using deep grids

arXiv: https://arxiv.org/abs/2006.14253 · HTML: https://mlsanity.com/paper/2006.14253/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2020.

## Built on by (most cited first, 13 of 13)

- [Differentiable Quality Diversity](https://mlsanity.com/paper/2106.03894/) (2021)
- [Multi-Objective Quality Diversity Optimization](https://mlsanity.com/paper/2202.03057/) (2022)
- [Training Diverse High-Dimensional Controllers by Scaling Covariance Matrix Adaptation MAP-Annealing](https://mlsanity.com/paper/2210.02622/) (2022)
- [Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning](https://mlsanity.com/paper/2211.02193/) (2022)
- [The Quality-Diversity Transformer: Generating Behavior-Conditioned Trajectories with Decision Transformers](https://mlsanity.com/paper/2303.16207/) (2023)
- [Quality Diversity under Sparse Reward and Sparse Interaction: Application to Grasping in Robotics](https://mlsanity.com/paper/2308.05483/) (2023)
- [How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity](https://mlsanity.com/paper/2202.08376/) (2022)
- [Gradient-Informed Quality Diversity for the Illumination of Discrete Spaces](https://mlsanity.com/paper/2306.05138/) (2023)
- [Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains](https://mlsanity.com/paper/2304.03672/) (2023)
- [Enhancing MAP-Elites with Multiple Parallel Evolution Strategies](https://mlsanity.com/paper/2303.06137/) (2023)
- [Quality-Diversity with Limited Resources](https://mlsanity.com/paper/2406.03731/) (2024)
- [Dynamic Quality-Diversity Search](https://mlsanity.com/paper/2404.05769/) (2024)
- [Contextual Quality-Diversity Evolutionary Reinforcement Learning for HVAC Control in Tropical Commercial Buildings](https://mlsanity.com/paper/2608.11324/) (2026)

## Abstract (arXiv)

Quality-Diversity optimisation algorithms enable the evolution of collections of both high-performing and diverse solutions. These collections offer the possibility to quickly adapt and switch from one solution to another in case it is not working as expected. It therefore finds many applications in real-world domain problems such as robotic control. However, QD algorithms, like most optimisation algorithms, are very sensitive to uncertainty on the fitness function, but also on the behavioural descriptors. Yet, such uncertainties are frequent in real-world applications. Few works have explored this issue in the specific case of QD algorithms, and inspired by the literature in Evolutionary Computation, mainly focus on using sampling to approximate the "true" value of the performances of a solution. However, sampling approaches require a high number of evaluations, which in many applications such as robotics, can quickly become impractical. In this work, we propose Deep-Grid MAP-Elites, a variant of the MAP-Elites algorithm that uses an archive of similar previously encountered solutions to approximate the performance of a solution. We compare our approach to previously explored ones on three noisy tasks: a standard optimisation task, the control of a redundant arm and a simulated Hexapod robot. The experimental results show that this simple approach is significantly more resilient to noise on the behavioural descriptors, while achieving competitive performances in terms of fitness optimisation, and being more sample-efficient than other existing approaches.