# AC2C: Adaptively Controlled Two-Hop Communication for Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2302.12515 · HTML: https://mlsanity.com/paper/2302.12515/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 33 references that are in the map.
- First posted on arXiv 24 Feb 2023.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [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)
- [The StarCraft Multi-Agent Challenge](https://mlsanity.com/paper/1902.04043/) (2019)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Learning Multiagent Communication with Backpropagation](https://mlsanity.com/paper/1605.07736/) (2016)
- [Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1610.03295/) (2016)
- [Emergence of Grounded Compositional Language in Multi-Agent Populations](https://mlsanity.com/paper/1703.04908/) (2017)
- [TarMAC: Targeted Multi-Agent Communication](https://mlsanity.com/paper/1810.11187/) (2018)
- [Learning Attentional Communication for Multi-Agent Cooperation](https://mlsanity.com/paper/1805.07733/) (2018)
- [Graph Convolutional Reinforcement Learning](https://mlsanity.com/paper/1810.09202/) (2018)
- [Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games](https://mlsanity.com/paper/1703.10069/) (2017)
- [PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning](https://mlsanity.com/paper/1809.03531/) (2018)
- [Learning Individually Inferred Communication for Multi-Agent Cooperation](https://mlsanity.com/paper/2006.06455/) (2020)
- [Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control](https://mlsanity.com/paper/1909.02682/) (2019)
- [GCS: Graph-based Coordination Strategy for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2201.06257/) (2022)
- [Learning Multi-agent Communication under Limited-bandwidth Restriction for Internet Packet Routing](https://mlsanity.com/paper/1903.05561/) (2019)

## Built on by (most cited first, 5 of 5)

- [Triple-BERT: Do We Really Need MARL for Order Dispatch on Ride-Sharing Platforms?](https://mlsanity.com/paper/2510.03257/) (2025)
- [Continuous-Time Value Iteration for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2509.09135/) (2025)
- [Communication Methods in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2601.12886/) (2026)
- [PC2P: Multi-Agent Path Finding via Personalized-Enhanced Communication and Crowd Perception](https://mlsanity.com/paper/2601.03301/) (2026)
- [Robust and Efficient Communication for Multi-Agent Learning](https://mlsanity.com/paper/2609.15361/) (2026)

## Abstract (arXiv)

Learning communication strategies in cooperative multi-agent reinforcement learning (MARL) has recently attracted intensive attention. Early studies typically assumed a fully-connected communication topology among agents, which induces high communication costs and may not be feasible. Some recent works have developed adaptive communication strategies to reduce communication overhead, but these methods cannot effectively obtain valuable information from agents that are beyond the communication range. In this paper, we consider a realistic communication model where each agent has a limited communication range, and the communication topology dynamically changes. To facilitate effective agent communication, we propose a novel communication protocol called Adaptively Controlled Two-Hop Communication (AC2C). After an initial local communication round, AC2C employs an adaptive two-hop communication strategy to enable long-range information exchange among agents to boost performance, which is implemented by a communication controller. This controller determines whether each agent should ask for two-hop messages and thus helps to reduce the communication overhead during distributed execution. We evaluate AC2C on three cooperative multi-agent tasks, and the experimental results show that it outperforms relevant baselines with lower communication costs.