# Learning Individually Inferred Communication for Multi-Agent Cooperation

arXiv: https://arxiv.org/abs/2006.06455 · HTML: https://mlsanity.com/paper/2006.06455/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

## Built on by (most cited first, 30 of 55)

- [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022)
- [ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind](https://mlsanity.com/paper/2111.09189/) (2021)
- [Cooperative Exploration for Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2107.11444/) (2021)
- [Towards a Standardised Performance Evaluation Protocol for Cooperative MARL](https://mlsanity.com/paper/2209.10485/) (2022)
- [Offline Decentralized Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2108.01832/) (2021)
- [Learning Selective Communication for Multi-Agent Path Finding](https://mlsanity.com/paper/2109.05413/) (2021)
- [Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?](https://mlsanity.com/paper/2305.17352/) (2023)
- [Semantic Channel Equalizer: Modelling Language Mismatch in Multi-User Semantic Communications](https://mlsanity.com/paper/2308.03789/) (2023)
- [Multi-Agent Coordination via Multi-Level Communication](https://mlsanity.com/paper/2209.12713/) (2022)
- [Cost-Effective Communication: An Auction-based Method for Language Agent Interaction](https://mlsanity.com/paper/2511.13193/) (2025)
- [Context-aware Communication for Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2312.15600/) (2023)
- [From Explicit Communication to Tacit Cooperation:A Novel Paradigm for Cooperative MARL](https://mlsanity.com/paper/2304.14656/) (2023)
- [T2MAC: Targeted and Trusted Multi-Agent Communication through Selective Engagement and Evidence-Driven Integration](https://mlsanity.com/paper/2401.10973/) (2024)
- [Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance](https://mlsanity.com/paper/2307.03119/) (2023)
- [Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2502.19717/) (2025)
- [E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance](https://mlsanity.com/paper/2212.02064/) (2022)
- [Scalable Communication for Multi-Agent Reinforcement Learning via Transformer-Based Email Mechanism](https://mlsanity.com/paper/2301.01919/) (2023)
- [Communication-Robust Multi-Agent Learning by Adaptable Auxiliary Multi-Agent Adversary Generation](https://mlsanity.com/paper/2305.05116/) (2023)
- [Settling Decentralized Multi-Agent Coordinated Exploration by Novelty Sharing](https://mlsanity.com/paper/2402.02097/) (2024)
- [Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaboration](https://mlsanity.com/paper/2404.03869/) (2024)
- [Learning to Share in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2112.08702/) (2021)
- [Dynamic Collaborative Multi-Agent Reinforcement Learning Communication for Autonomous Drone Reforestation](https://mlsanity.com/paper/2211.15414/) (2022)
- [AC2C: Adaptively Controlled Two-Hop Communication for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2302.12515/) (2023)
- [Towards True Lossless Sparse Communication in Multi-Agent Systems](https://mlsanity.com/paper/2212.00115/) (2022)
- [Minimizing Communication while Maximizing Performance in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2106.08482/) (2021)
- [Robust Multi-agent Communication via Multi-view Message Certification](https://mlsanity.com/paper/2305.13936/) (2023)
- [Policy Compatible Skill Incremental Learning via Lazy Learning Interface](https://mlsanity.com/paper/2509.20612/) (2025)
- [Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2102.12957/) (2021)
- [Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game](https://mlsanity.com/paper/2206.01451/) (2022)
- [Efficient Communication via Self-supervised Information Aggregation for Online and Offline Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2302.09605/) (2023)

## Abstract (arXiv)

Communication lays the foundation for human cooperation. It is also crucial for multi-agent cooperation. However, existing work focuses on broadcast communication, which is not only impractical but also leads to information redundancy that could even impair the learning process. To tackle these difficulties, we propose Individually Inferred Communication (I2C), a simple yet effective model to enable agents to learn a prior for agent-agent communication. The prior knowledge is learned via causal inference and realized by a feed-forward neural network that maps the agent's local observation to a belief about who to communicate with. The influence of one agent on another is inferred via the joint action-value function in multi-agent reinforcement learning and quantified to label the necessity of agent-agent communication. Furthermore, the agent policy is regularized to better exploit communicated messages. Empirically, we show that I2C can not only reduce communication overhead but also improve the performance in a variety of multi-agent cooperative scenarios, comparing to existing methods. The code is available at https://github.com/PKU-AI-Edge/I2C.