# Evolutionarily-Curated Curriculum Learning for Deep Reinforcement Learning Agents

arXiv: https://arxiv.org/abs/1901.05431 · HTML: https://mlsanity.com/paper/1901.05431/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2019.

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

- [Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning](https://mlsanity.com/paper/2105.14780/) (2021)
- [Reliable validation of Reinforcement Learning Benchmarks](https://mlsanity.com/paper/2203.01075/) (2022)

## Abstract (arXiv)

In this paper we propose a new training loop for deep reinforcement learning agents with an evolutionary generator. Evolutionary procedural content generation has been used in the creation of maps and levels for games before. Our system incorporates an evolutionary map generator to construct a training curriculum that is evolved to maximize loss within the state-of-the-art Double Dueling Deep Q Network architecture with prioritized replay. We present a case-study in which we prove the efficacy of our new method on a game with a discrete, large action space we made called Attackers and Defenders. Our results demonstrate that training on an evolutionarily-curated curriculum (directed sampling) of maps both expedites training and improves generalization when compared to a network trained on an undirected sampling of maps.