# Genetic-Gated Networks for Deep Reinforcement

arXiv: https://arxiv.org/abs/1903.01886 · HTML: https://mlsanity.com/paper/1903.01886/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2018.

## Built on by (most cited first, 8 of 8)

- [Symbolic Regression via Neural-Guided Genetic Programming Population Seeding](https://mlsanity.com/paper/2111.00053/) (2021)
- [Deep Generative Symbolic Regression](https://mlsanity.com/paper/2401.00282/) (2023)
- [Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms](https://mlsanity.com/paper/2401.11963/) (2024)
- [Combining Evolution and Deep Reinforcement Learning for Policy Search: a Survey](https://mlsanity.com/paper/2203.14009/) (2022)
- [MAGMA: An Optimization Framework for Mapping Multiple DNNs on Multiple Accelerator Cores](https://mlsanity.com/paper/2104.13997/) (2021)
- [Making Reinforcement Learning Work on Swimmer](https://mlsanity.com/paper/2208.07587/) (2022)
- [Evolutionary Reinforcement Learning: A Systematic Review and Future Directions](https://mlsanity.com/paper/2402.13296/) (2024)
- [Cogment: Open Source Framework For Distributed Multi-actor Training, Deployment & Operations](https://mlsanity.com/paper/2106.11345/) (2021)

## Abstract (arXiv)

We introduce the Genetic-Gated Networks (G2Ns), simple neural networks that combine a gate vector composed of binary genetic genes in the hidden layer(s) of networks. Our method can take both advantages of gradient-free optimization and gradient-based optimization methods, of which the former is effective for problems with multiple local minima, while the latter can quickly find local minima. In addition, multiple chromosomes can define different models, making it easy to construct multiple models and can be effectively applied to problems that require multiple models. We show that this G2N can be applied to typical reinforcement learning algorithms to achieve a large improvement in sample efficiency and performance.