# Automated Curriculum Learning for Embodied Agents: A Neuroevolutionary Approach

arXiv: https://arxiv.org/abs/2102.08849 · HTML: https://mlsanity.com/paper/2102.08849/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2021.

## Built on by (most cited first, 5 of 5)

- [Boost Your Human Image Generation Model via Direct Preference Optimization](https://mlsanity.com/paper/2405.20216/) (2024)
- [A Psychology-based Unified Dynamic Framework for Curriculum Learning](https://mlsanity.com/paper/2408.05326/) (2024)
- [Bridging VLMs and Embodied Intelligence with Deliberate Practice Policy Optimization](https://mlsanity.com/paper/2511.16602/) (2025)
- [Qualitative Differences Between Evolutionary Strategies and Reinforcement Learning Methods for Control of Autonomous Agents](https://mlsanity.com/paper/2205.07592/) (2022)
- [CL4KGE: A Curriculum Learning Method for Knowledge Graph Embedding](https://mlsanity.com/paper/2408.14840/) (2024)

## Abstract (arXiv)

We demonstrate how an evolutionary algorithm can be extended with a curriculum learning process that selects automatically the environmental conditions in which the evolving agents are evaluated. The environmental conditions are selected so to adjust the level of difficulty to the ability level of the current evolving agents and so to challenge the weaknesses of the evolving agents. The method does not require domain knowledge and does not introduce additional hyperparameters. The results collected on two benchmark problems, that require to solve a task in significantly varying environmental conditions, demonstrate that the method proposed outperforms conventional algorithms and generates solutions that are robust to variations