# Concentration Network for Reinforcement Learning of Large-Scale Multi-Agent Systems

arXiv: https://arxiv.org/abs/2203.06416 · HTML: https://mlsanity.com/paper/2203.06416/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 39 references that are in the map.
- First posted on arXiv 12 Mar 2022.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [Value-Decomposition Networks For Cooperative Multi-Agent Learning](https://mlsanity.com/paper/1706.05296/) (2017)
- [QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1905.05408/) (2019)
- [Emergent Tool Use From Multi-Agent Autocurricula](https://mlsanity.com/paper/1909.07528/) (2019)
- [StarCraft II: A New Challenge for Reinforcement Learning](https://mlsanity.com/paper/1708.04782/) (2017)
- [Actor-Attention-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1810.02912/) (2018)
- [Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2006.10800/) (2020)
- [Multiagent Cooperation and Competition with Deep Reinforcement Learning](https://mlsanity.com/paper/1511.08779/) (2015)
- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [Mastering Complex Control in MOBA Games with Deep Reinforcement Learning](https://mlsanity.com/paper/1912.09729/) (2019)
- [Graph Convolutional Reinforcement Learning](https://mlsanity.com/paper/1810.09202/) (2018)
- [MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence](https://mlsanity.com/paper/1712.00600/) (2017)
- [DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning](https://mlsanity.com/paper/2106.06135/) (2021)
- [VAIN: Attentional Multi-agent Predictive Modeling](https://mlsanity.com/paper/1706.06122/) (2017)
- [Learning Transferable Cooperative Behavior in Multi-Agent Teams](https://mlsanity.com/paper/1906.01202/) (2019)
- [Q-value Path Decomposition for Deep Multiagent Reinforcement Learning](https://mlsanity.com/paper/2002.03950/) (2020)
- [Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling](https://mlsanity.com/paper/1801.10296/) (2018)
- [Natural Emergence of Heterogeneous Strategies in Artificially Intelligent Competitive Teams](https://mlsanity.com/paper/2007.03102/) (2020)
- [Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks](https://mlsanity.com/paper/1609.02993/) (2016)
- [Technical Problems With "Programmable self-assembly in a thousand-robot swarm"](https://mlsanity.com/paper/1708.03341/) (2017)

## Built on by (most cited first, 7 of 7)

- [Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2207.03902/) (2022)
- [Guiding Multi-agent Multi-task Reinforcement Learning by a Hierarchical Framework with Logical Reward Shaping](https://mlsanity.com/paper/2411.01184/) (2024)
- [A Policy Resonance Approach to Solve the Problem of Responsibility Diffusion in Multiagent Reinforcement Learning](https://mlsanity.com/paper/2208.07753/) (2022)
- [Learning Heterogeneous Agent Cooperation via Multiagent League Training](https://mlsanity.com/paper/2211.11616/) (2022)
- [Self-Clustering Hierarchical Multi-Agent Reinforcement Learning with Extensible Cooperation Graph](https://mlsanity.com/paper/2403.18056/) (2024)
- [Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review](https://mlsanity.com/paper/2507.10142/) (2025)
- [Prioritized League Reinforcement Learning for Large-Scale Heterogeneous Multiagent Systems](https://mlsanity.com/paper/2403.18057/) (2024)

## Abstract (arXiv)

When dealing with a series of imminent issues, humans can naturally concentrate on a subset of these concerning issues by prioritizing them according to their contributions to motivational indices, e.g., the probability of winning a game. This idea of concentration offers insights into reinforcement learning of sophisticated Large-scale Multi-Agent Systems (LMAS) participated by hundreds of agents. In such an LMAS, each agent receives a long series of entity observations at each step, which can overwhelm existing aggregation networks such as graph attention networks and cause inefficiency. In this paper, we propose a concentration network called ConcNet. First, ConcNet scores the observed entities considering several motivational indices, e.g., expected survival time and state value of the agents, and then ranks, prunes, and aggregates the encodings of observed entities to extract features. Second, distinct from the well-known attention mechanism, ConcNet has a unique motivational subnetwork to explicitly consider the motivational indices when scoring the observed entities. Furthermore, we present a concentration policy gradient architecture that can learn effective policies in LMAS from scratch. Extensive experiments demonstrate that the presented architecture has excellent scalability and flexibility, and significantly outperforms existing methods on LMAS benchmarks.