# Multi-Emitter MAP-Elites: Improving quality, diversity and convergence speed with heterogeneous sets of emitters

arXiv: https://arxiv.org/abs/2007.05352 · HTML: https://mlsanity.com/paper/2007.05352/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2020.

## Built on by (most cited first, 14 of 14)

- [Combinatorial Optimization with Policy Adaptation using Latent Space Search](https://mlsanity.com/paper/2311.13569/) (2023)
- [Covariance Matrix Adaptation MAP-Annealing](https://mlsanity.com/paper/2205.10752/) (2022)
- [Dynamics-Aware Quality-Diversity for Efficient Learning of Skill Repertoires](https://mlsanity.com/paper/2109.08522/) (2021)
- [Training Diverse High-Dimensional Controllers by Scaling Covariance Matrix Adaptation MAP-Annealing](https://mlsanity.com/paper/2210.02622/) (2022)
- [Large Language Models as In-context AI Generators for Quality-Diversity](https://mlsanity.com/paper/2404.15794/) (2024)
- [Discovering and Exploiting Sparse Rewards in a Learned Behavior Space](https://mlsanity.com/paper/2111.01919/) (2021)
- [Multi-Objective Quality-Diversity for Crystal Structure Prediction](https://mlsanity.com/paper/2403.17164/) (2024)
- [Evolving Flying Machines in Minecraft Using Quality Diversity](https://mlsanity.com/paper/2302.00782/) (2023)
- [A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning Models](https://mlsanity.com/paper/2204.14061/) (2022)
- [Quality-Diversity Meta-Evolution: customising behaviour spaces to a meta-objective](https://mlsanity.com/paper/2109.03918/) (2021)
- [Improving the Data Efficiency of Multi-Objective Quality-Diversity through Gradient Assistance and Crowding Exploration](https://mlsanity.com/paper/2302.12668/) (2023)
- [Enhancing MAP-Elites with Multiple Parallel Evolution Strategies](https://mlsanity.com/paper/2303.06137/) (2023)
- [Quality-diversity in dissimilarity spaces](https://mlsanity.com/paper/2211.12337/) (2022)
- [Dynamic Quality-Diversity Search](https://mlsanity.com/paper/2404.05769/) (2024)

## Abstract (arXiv)

Quality-Diversity (QD) optimisation is a new family of learning algorithms that aims at generating collections of diverse and high-performing solutions. Among those algorithms, the recently introduced Covariance Matrix Adaptation MAP-Elites (CMA-ME) algorithm proposes the concept of emitters, which uses a predefined heuristic to drive the algorithm's exploration. This algorithm was shown to outperform MAP-Elites, a popular QD algorithm that has demonstrated promising results in numerous applications. In this paper, we introduce Multi-Emitter MAP-Elites (ME-MAP-Elites), an algorithm that directly extends CMA-ME and improves its quality, diversity and data efficiency. It leverages the diversity of a heterogeneous set of emitters, in which each emitter type improves the optimisation process in different ways. A bandit algorithm dynamically finds the best selection of emitters depending on the current situation. We evaluate the performance of ME-MAP-Elites on six tasks, ranging from standard optimisation problems (in 100 dimensions) to complex locomotion tasks in robotics. Our comparisons against CMA-ME and MAP-Elites show that ME-MAP-Elites is faster at providing collections of solutions that are significantly more diverse and higher performing. Moreover, in cases where no fruitful synergy can be found between the different emitters, ME-MAP-Elites is equivalent to the best of the compared algorithms.