# Bayesian Optimization Framework for Efficient Fleet Design in Autonomous Multi-Robot Exploration

arXiv: https://arxiv.org/abs/2408.11751 · HTML: https://mlsanity.com/paper/2408.11751/

## Key facts

- Cited by 1 papers in the map (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 16 of its 39 references that are in the map.
- First posted on arXiv 21 Aug 2024.

## 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, 16 of 16)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Convex Optimization: Algorithms and Complexity](https://mlsanity.com/paper/1405.4980/) (2014)
- [Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward](https://mlsanity.com/paper/2006.06626/) (2020)
- [Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2204.05944/) (2022)
- [Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization](https://mlsanity.com/paper/2210.10199/) (2022)
- [Heterogeneous Multi-Robot Reinforcement Learning](https://mlsanity.com/paper/2301.07137/) (2023)
- [Deep Reinforcement Learning for Decentralized Multi-Robot Exploration With Macro Actions](https://mlsanity.com/paper/2110.02181/) (2021)
- [Coordinating the Crowd: Inducing Desirable Equilibria in Non-Cooperative Systems](https://mlsanity.com/paper/1901.10923/) (2019)
- [Reward Design for Driver Repositioning Using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2002.06723/) (2020)
- [Mine Tunnel Exploration using Multiple Quadrupedal Robots](https://mlsanity.com/paper/1909.09662/) (2019)
- [A Robust Multi-Objective Bayesian Optimization Framework Considering Input Uncertainty](https://mlsanity.com/paper/2202.12848/) (2022)
- [Autonomous Teamed Exploration of Subterranean Environments using Legged and Aerial Robots](https://mlsanity.com/paper/2111.06482/) (2021)
- [ACHORD: Communication-Aware Multi-Robot Coordination with Intermittent Connectivity](https://mlsanity.com/paper/2206.02245/) (2022)
- [Rapid and High-Fidelity Subsurface Exploration with Multiple Aerial Robots](https://mlsanity.com/paper/2012.10788/) (2020)
- [Non-Monotone Energy-Aware Information Gathering for Heterogeneous Robot Teams](https://mlsanity.com/paper/2101.11093/) (2021)
- [Investigating the Effect of Deictic Movements of a Multi-robot](https://mlsanity.com/paper/2006.03805/) (2020)

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

- [Hypernetwork-based approach for optimal composition design in partially controlled multi-agent systems](https://mlsanity.com/paper/2502.12605/) (2025)

## Abstract (arXiv)

This study addresses the challenge of fleet design optimization in the context of heterogeneous multi-robot fleets, aiming to obtain feasible designs that balance performance and costs. In the domain of autonomous multi-robot exploration, reinforcement learning agents play a central role, offering adaptability to complex terrains and facilitating collaboration among robots. However, modifying the fleet composition results in changes in the learned behavior, and training multi-robot systems using multi-agent reinforcement learning is expensive. Therefore, an exhaustive evaluation of each potential fleet design is infeasible. To tackle these hurdles, we introduce Bayesian Optimization for Fleet Design (BOFD), a framework leveraging multi-objective Bayesian Optimization to explore fleets on the Pareto front of performance and cost while accounting for uncertainty in the design space. Moreover, we establish a sub-linear bound for cumulative regret, supporting BOFD's robustness and efficacy. Extensive benchmark experiments in synthetic and simulated environments demonstrate the superiority of our framework over state-of-the-art methods, achieving efficient fleet designs with minimal fleet evaluations.