# Visual Swarm Navigation via Deep Reinforcement Learning and Evolutionary Hybrid Design

arXiv: https://arxiv.org/abs/2610.06400 · HTML: https://mlsanity.com/paper/2610.06400/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 18 of its 65 references that are in the map.
- First posted on arXiv 5 Oct 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 18 of 18)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [Generative AI](https://mlsanity.com/paper/2309.07930/) (2023)
- [Robot Learning from Randomized Simulations: A Review](https://mlsanity.com/paper/2111.00956/) (2021)
- [Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey](https://mlsanity.com/paper/2011.13112/) (2020)
- [Deep Reinforcement Learning for Swarm Systems](https://mlsanity.com/paper/1807.06613/) (2018)
- [Deep Learning in Robotics: A Review of Recent Research](https://mlsanity.com/paper/1707.07217/) (2017)
- [Collective Intelligence for Deep Learning: A Survey of Recent Developments](https://mlsanity.com/paper/2111.14377/) (2021)
- [Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning](https://mlsanity.com/paper/2109.07735/) (2021)
- [Control Transformer: Robot Navigation in Unknown Environments through PRM-Guided Return-Conditioned Sequence Modeling](https://mlsanity.com/paper/2211.06407/) (2022)
- [PASTA: Pretrained Action-State Transformer Agents](https://mlsanity.com/paper/2307.10936/) (2023)
- [Model-Based Stochastic Search for Large Scale Optimization of Multi-Agent UAV Swarms](https://mlsanity.com/paper/1803.01106/) (2018)
- [Past, Present, and Future of Swarm Robotics](https://mlsanity.com/paper/2101.00671/) (2021)
- [Automated Problem Identification: Regression vs Classification via Evolutionary Deep Networks](https://mlsanity.com/paper/1707.00703/) (2017)
- [A Study of Reinforcement Learning Algorithms for Aggregates of Minimalistic Robots](https://mlsanity.com/paper/2203.15129/) (2022)
- [Learning Policies for Dynamic Coalition Formation in Multi-Robot Task Allocation](https://mlsanity.com/paper/2412.20397/) (2024)

## Abstract (arXiv)

Swarm robotics presents a robust and cost-effective paradigm for advanced automation in complex, dynamic environments, such as those encountered in search and rescue or environmental monitoring. A fundamental challenge for this field is the data-driven design of decentralized controllers capable of generating emergent collective behaviors. This paper proposes a novel, AI-driven hybrid methodology for the automatic synthesis of swarm robotic controllers for autonomous visual navigation. This approach synergistically combines multi-agent reinforcement learning with neuro-evolutionary strategies, specifically leveraging implementations of the cross-entropy method and the covariance matrix adaptation evolution strategy to optimize a pre-trained individual navigation policy. The underlying deep architecture is engineered for low-cost, resource-constrained platforms, utilizing a compact neural network that relies exclusively on monocular camera imagery. This vision-based design emphasizes computational and energy efficiency, a critical requirement for practical swarm deployments. Experiments, performed in a high-fidelity physics simulator, demonstrate that the resulting controllers enable robust and scalable collective exploration of diverse indoor environments. The controller trained using our cross-entropy method achieves superior exploration coverage, visiting 36.20% more regions compared to the covariance matrix adaptation evolution strategy. Critically, our best vision-based policy achieves exploration performance statistically comparable to traditional methods relying on more expensive distance sensors, while delivering a significant 31.40% average reduction in energy consumption. These findings validate an effective and economically viable autonomous control system, establishing a path for deploying highly efficient collective intelligence in real-world engineering applications.