# T2MAC: Targeted and Trusted Multi-Agent Communication through Selective Engagement and Evidence-Driven Integration

arXiv: https://arxiv.org/abs/2401.10973 · HTML: https://mlsanity.com/paper/2401.10973/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 34 references that are in the map.
- First posted on arXiv 19 Jan 2024.

## Builds on (most cited first, 22 of 22)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [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)
- [Predictive Uncertainty Estimation via Prior Networks](https://mlsanity.com/paper/1802.10501/) (2018)
- [Deep Exploration via Bootstrapped DQN](https://mlsanity.com/paper/1602.04621/) (2016)
- [Value-Decomposition Networks For Cooperative Multi-Agent Learning](https://mlsanity.com/paper/1706.05296/) (2017)
- [Learning Multiagent Communication with Backpropagation](https://mlsanity.com/paper/1605.07736/) (2016)
- [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)
- [Ensemble Distribution Distillation](https://mlsanity.com/paper/1905.00076/) (2019)
- [Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks](https://mlsanity.com/paper/1812.09755/) (2018)
- [Off-Policy Multi-Agent Decomposed Policy Gradients](https://mlsanity.com/paper/2007.12322/) (2020)
- [Learning to Schedule Communication in Multi-agent Reinforcement Learning](https://mlsanity.com/paper/1902.01554/) (2019)
- [Learning Nearly Decomposable Value Functions Via Communication Minimization](https://mlsanity.com/paper/1910.05366/) (2019)
- [Learning Efficient Multi-agent Communication: An Information Bottleneck Approach](https://mlsanity.com/paper/1911.06992/) (2019)
- [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)
- [Learning Agent Communication under Limited Bandwidth by Message Pruning](https://mlsanity.com/paper/1912.05304/) (2019)
- [Succinct and Robust Multi-Agent Communication With Temporal Message Control](https://mlsanity.com/paper/2010.14391/) (2020)

## Built on by (most cited first, 10 of 10)

- [Seven Security Challenges in Cross-domain Multi-agent LLM Systems](https://mlsanity.com/paper/2505.23847/) (2025)
- [Interactive Learning for LLM Reasoning](https://mlsanity.com/paper/2509.26306/) (2025)
- [Retaining Suboptimal Actions to Follow Shifting Optima in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2602.17062/) (2026)
- [LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2605.18077/) (2026)
- [HiComm: Hierarchical Communication for Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2606.29126/) (2026)
- [AC-MASAC: An Attentive Curriculum Learning Framework for Heterogeneous UAV Swarm Coordination](https://mlsanity.com/paper/2602.11735/) (2026)
- [Communication Gain and Delay Cost Under Cross-Timestep Delays in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2604.03785/) (2026)
- [PEAR: Permutation-Equivariant Adaptive Routing Multi-Agent Debate](https://mlsanity.com/paper/2606.20621/) (2026)
- [MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination](https://mlsanity.com/paper/2607.03473/) (2026)
- [When to Communicate: Belief Distributions and KL Divergence for Principled Gating in Multi-Agent RL](https://mlsanity.com/paper/2608.14559/) (2026)

## Abstract (arXiv)

Communication stands as a potent mechanism to harmonize the behaviors of multiple agents. However, existing works primarily concentrate on broadcast communication, which not only lacks practicality, but also leads to information redundancy. This surplus, one-fits-all information could adversely impact the communication efficiency. Furthermore, existing works often resort to basic mechanisms to integrate observed and received information, impairing the learning process. To tackle these difficulties, we propose Targeted and Trusted Multi-Agent Communication (T2MAC), a straightforward yet effective method that enables agents to learn selective engagement and evidence-driven integration. With T2MAC, agents have the capability to craft individualized messages, pinpoint ideal communication windows, and engage with reliable partners, thereby refining communication efficiency. Following the reception of messages, the agents integrate information observed and received from different sources at an evidence level. This process enables agents to collectively use evidence garnered from multiple perspectives, fostering trusted and cooperative behaviors. We evaluate our method on a diverse set of cooperative multi-agent tasks, with varying difficulties, involving different scales and ranging from Hallway, MPE to SMAC. The experiments indicate that the proposed model not only surpasses the state-of-the-art methods in terms of cooperative performance and communication efficiency, but also exhibits impressive generalization.