# Deep Reinforcement Learning for Swarm Systems

arXiv: https://arxiv.org/abs/1807.06613 · HTML: https://mlsanity.com/paper/1807.06613/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2018.

## Built on by (most cited first, 30 of 45)

- [Cooperative Exploration for Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2107.11444/) (2021)
- [MARFT: Multi-Agent Reinforcement Fine-Tuning](https://mlsanity.com/paper/2504.16129/) (2025)
- [Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence](https://mlsanity.com/paper/2302.03322/) (2023)
- [Swarm Reinforcement Learning For Adaptive Mesh Refinement](https://mlsanity.com/paper/2304.00818/) (2023)
- [A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2209.03859/) (2022)
- [Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks](https://mlsanity.com/paper/2407.08164/) (2024)
- [A Survey on Reinforcement Learning Methods in Character Animation](https://mlsanity.com/paper/2203.04735/) (2022)
- [Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2211.15612/) (2022)
- [Multi-robot Cooperative Pursuit via Potential Field-Enhanced Reinforcement Learning](https://mlsanity.com/paper/2203.04700/) (2022)
- [TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems](https://mlsanity.com/paper/2301.05334/) (2023)
- [Agent-Time Attention for Sparse Rewards Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2210.17540/) (2022)
- [Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization](https://mlsanity.com/paper/2310.09833/) (2023)
- [Autonomous Drone Swarm Navigation and Multi-target Tracking in 3D Environments with Dynamic Obstacles](https://mlsanity.com/paper/2202.06253/) (2022)
- [Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models](https://mlsanity.com/paper/2409.05709/) (2024)
- [Online Planning for Multi-UAV Pursuit-Evasion in Unknown Environments Using Deep Reinforcement Learning](https://mlsanity.com/paper/2409.15866/) (2024)
- [Nearest-Neighbor-based Collision Avoidance for Quadrotors via Reinforcement Learning](https://mlsanity.com/paper/2104.14912/) (2021)
- [Multi-agent Actor-Critic with Time Dynamical Opponent Model](https://mlsanity.com/paper/2204.05576/) (2022)
- [Multi-Agent Guided Policy Optimization](https://mlsanity.com/paper/2507.18059/) (2025)
- [Understanding reinforcement learned crowds](https://mlsanity.com/paper/2209.09344/) (2022)
- [Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics through Multi-Agent Reinforcement Learning Algorithms](https://mlsanity.com/paper/2401.07056/) (2024)
- [Sim-Env: Decoupling OpenAI Gym Environments from Simulation Models](https://mlsanity.com/paper/2102.09824/) (2021)
- [Learning to Play Soccer From Scratch: Sample-Efficient Emergent Coordination through Curriculum-Learning and Competition](https://mlsanity.com/paper/2103.05174/) (2021)
- [Resilient Consensus-based Multi-agent Reinforcement Learning with Function Approximation](https://mlsanity.com/paper/2111.06776/) (2021)
- [Scalable Task-Driven Robotic Swarm Control via Collision Avoidance and Learning Mean-Field Control](https://mlsanity.com/paper/2209.07420/) (2022)
- [A Dual Curriculum Learning Framework for Multi-UAV Pursuit-Evasion in Diverse Environments](https://mlsanity.com/paper/2312.12255/) (2023)
- [A Prompt-driven Task Planning Method for Multi-drones based on Large Language Model](https://mlsanity.com/paper/2406.00006/) (2024)
- [GenAI-based Multi-Agent Reinforcement Learning towards Distributed Agent Intelligence: A Generative-RL Agent Perspective](https://mlsanity.com/paper/2507.09495/) (2025)
- [HypeMARL: Multi-Agent Reinforcement Learning For High-Dimensional, Parametric, and Distributed Systems](https://mlsanity.com/paper/2509.16709/) (2025)
- [NQMIX: Non-monotonic Value Function Factorization for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2104.01939/) (2021)
- [Exploiting Semantic Epsilon Greedy Exploration Strategy in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2201.10803/) (2022)

## Abstract (arXiv)

Recently, deep reinforcement learning (RL) methods have been applied successfully to multi-agent scenarios. Typically, these methods rely on a concatenation of agent states to represent the information content required for decentralized decision making. However, concatenation scales poorly to swarm systems with a large number of homogeneous agents as it does not exploit the fundamental properties inherent to these systems: (i) the agents in the swarm are interchangeable and (ii) the exact number of agents in the swarm is irrelevant. Therefore, we propose a new state representation for deep multi-agent RL based on mean embeddings of distributions. We treat the agents as samples of a distribution and use the empirical mean embedding as input for a decentralized policy. We define different feature spaces of the mean embedding using histograms, radial basis functions and a neural network learned end-to-end. We evaluate the representation on two well known problems from the swarm literature (rendezvous and pursuit evasion), in a globally and locally observable setup. For the local setup we furthermore introduce simple communication protocols. Of all approaches, the mean embedding representation using neural network features enables the richest information exchange between neighboring agents facilitating the development of more complex collective strategies.