# MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence

arXiv: https://arxiv.org/abs/1712.00600 · HTML: https://mlsanity.com/paper/1712.00600/

## Key facts

- Cited by 71 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2017.

## Built on by (most cited first, 30 of 71)

- [Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificates](https://mlsanity.com/paper/2101.05436/) (2021)
- [VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning](https://mlsanity.com/paper/2207.03530/) (2022)
- [UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers](https://mlsanity.com/paper/2101.08001/) (2021)
- [Multi-agent Dynamic Algorithm Configuration](https://mlsanity.com/paper/2210.06835/) (2022)
- [Multi-agent Reinforcement Learning: A Comprehensive Survey](https://mlsanity.com/paper/2312.10256/) (2023)
- [MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library](https://mlsanity.com/paper/2210.13708/) (2022)
- [POGEMA: A Benchmark Platform for Cooperative Multi-Agent Pathfinding](https://mlsanity.com/paper/2407.14931/) (2024)
- [AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-Making](https://mlsanity.com/paper/2411.03865/) (2024)
- [Collaborative Visual Navigation](https://mlsanity.com/paper/2107.01151/) (2021)
- [Influencing Long-Term Behavior in Multiagent Reinforcement Learning](https://mlsanity.com/paper/2203.03535/) (2022)
- [The Neural MMO Platform for Massively Multiagent Research](https://mlsanity.com/paper/2110.07594/) (2021)
- [An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility](https://mlsanity.com/paper/2203.07676/) (2022)
- [RoMFAC: A robust mean-field actor-critic reinforcement learning against adversarial perturbations on states](https://mlsanity.com/paper/2205.07229/) (2022)
- [A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2209.03859/) (2022)
- [FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2406.02081/) (2024)
- [Emergent Language: A Survey and Taxonomy](https://mlsanity.com/paper/2409.02645/) (2024)
- [Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL](https://mlsanity.com/paper/2207.05683/) (2022)
- [Individual-Level Inverse Reinforcement Learning for Mean Field Games](https://mlsanity.com/paper/2202.06401/) (2022)
- [Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2210.03022/) (2022)
- [Concentration Network for Reinforcement Learning of Large-Scale Multi-Agent Systems](https://mlsanity.com/paper/2203.06416/) (2022)
- [Partially Observable Mean Field Multi-Agent Reinforcement Learning Based on Graph-Attention](https://mlsanity.com/paper/2304.12653/) (2023)
- [Learning Decentralized Partially Observable Mean Field Control for Artificial Collective Behavior](https://mlsanity.com/paper/2307.06175/) (2023)
- [Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2502.19717/) (2025)
- [POGEMA: Partially Observable Grid Environment for Multiple Agents](https://mlsanity.com/paper/2206.10944/) (2022)
- [Fictitious Cross-Play: Learning Global Nash Equilibrium in Mixed Cooperative-Competitive Games](https://mlsanity.com/paper/2310.03354/) (2023)
- [Rethinking Agent Design: From Top-Down Workflows to Bottom-Up Skill Evolution](https://mlsanity.com/paper/2505.17673/) (2025)
- [Learning to Share in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2112.08702/) (2021)
- [XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library](https://mlsanity.com/paper/2312.16248/) (2023)
- [An Extended Benchmarking of Multi-Agent Reinforcement Learning Algorithms in Complex Fully Cooperative Tasks](https://mlsanity.com/paper/2502.04773/) (2025)
- [Emergent collective intelligence from massive-agent cooperation and competition](https://mlsanity.com/paper/2301.01609/) (2023)

## Abstract (arXiv)

We introduce MAgent, a platform to support research and development of many-agent reinforcement learning. Unlike previous research platforms on single or multi-agent reinforcement learning, MAgent focuses on supporting the tasks and the applications that require hundreds to millions of agents. Within the interactions among a population of agents, it enables not only the study of learning algorithms for agents' optimal polices, but more importantly, the observation and understanding of individual agent's behaviors and social phenomena emerging from the AI society, including communication languages, leaderships, altruism. MAgent is highly scalable and can host up to one million agents on a single GPU server. MAgent also provides flexible configurations for AI researchers to design their customized environments and agents. In this demo, we present three environments designed on MAgent and show emerged collective intelligence by learning from scratch.