# Direct Mutation and Crossover in Genetic Algorithms Applied to Reinforcement Learning Tasks

arXiv: https://arxiv.org/abs/2201.04815 · HTML: https://mlsanity.com/paper/2201.04815/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 13 Jan 2022.

## Builds on (most cited first, 7 of 7)

- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [Population Based Training of Neural Networks](https://mlsanity.com/paper/1711.09846/) (2017)
- [Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science](https://mlsanity.com/paper/1707.04780/) (2017)
- [Human-level performance in first-person multiplayer games with population-based deep reinforcement learning](https://mlsanity.com/paper/1807.01281/) (2018)
- [Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning](https://mlsanity.com/paper/1712.06567/) (2017)
- [Hypothesis-based Belief Planning for Dexterous Grasping](https://mlsanity.com/paper/1903.05517/) (2019)
- [Dyna-T: Dyna-Q and Upper Confidence Bounds Applied to Trees](https://mlsanity.com/paper/2201.04502/) (2022)

## Built on by (most cited first, 1 of 1)

- [Graph Neural Networks as Ordering Heuristics for Parallel Graph Coloring](https://mlsanity.com/paper/2408.05054/) (2024)

## Abstract (arXiv)

Neuroevolution has recently been shown to be quite competitive in reinforcement learning (RL) settings, and is able to alleviate some of the drawbacks of gradient-based approaches. This paper will focus on applying neuroevolution using a simple genetic algorithm (GA) to find the weights of a neural network that produce optimally behaving agents. In addition, we present two novel modifications that improve the data efficiency and speed of convergence when compared to the initial implementation. The modifications are evaluated on the FrozenLake environment provided by OpenAI gym and prove to be significantly better than the baseline approach.