# Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/1912.01160 · HTML: https://mlsanity.com/paper/1912.01160/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2019.

## Built on by (most cited first, 17 of 17)

- [Celebrating Diversity in Shared Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2106.02195/) (2021)
- [SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition](https://mlsanity.com/paper/2111.08857/) (2021)
- [Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?](https://mlsanity.com/paper/2305.17352/) (2023)
- [MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering](https://mlsanity.com/paper/2303.18157/) (2023)
- [Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2207.03902/) (2022)
- [CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control](https://mlsanity.com/paper/2405.17152/) (2024)
- [Dealing with Non-Stationarity in MARL via Trust-Region Decomposition](https://mlsanity.com/paper/2102.10616/) (2021)
- [Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis](https://mlsanity.com/paper/2206.09046/) (2022)
- [S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?](https://mlsanity.com/paper/2206.11054/) (2022)
- [SEA: A Spatially Explicit Architecture for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2304.12532/) (2023)
- [Social Environment Design](https://mlsanity.com/paper/2402.14090/) (2024)
- [NGENT: Next-Generation AI Agents Must Integrate Multi-Domain Abilities to Achieve Artificial General Intelligence](https://mlsanity.com/paper/2504.21433/) (2025)
- [Structured Diversification Emergence via Reinforced Organization Control and Hierarchical Consensus Learning](https://mlsanity.com/paper/2102.04775/) (2021)
- [Decentralized Circle Formation Control for Fish-like Robots in the Real-world via Reinforcement Learning](https://mlsanity.com/paper/2103.05293/) (2021)
- [A Policy Resonance Approach to Solve the Problem of Responsibility Diffusion in Multiagent Reinforcement Learning](https://mlsanity.com/paper/2208.07753/) (2022)
- [Progression Cognition Reinforcement Learning with Prioritized Experience for Multi-Vehicle Pursuit](https://mlsanity.com/paper/2306.05016/) (2023)
- [Communication Learning in Multi-Agent Systems from Graph Modeling Perspective](https://mlsanity.com/paper/2411.00382/) (2024)

## Abstract (arXiv)

Social psychology and real experiences show that cognitive consistency plays an important role to keep human society in order: if people have a more consistent cognition about their environments, they are more likely to achieve better cooperation. Meanwhile, only cognitive consistency within a neighborhood matters because humans only interact directly with their neighbors. Inspired by these observations, we take the first step to introduce neighborhood cognitive consistency (NCC) into multi-agent reinforcement learning (MARL). Our NCC design is quite general and can be easily combined with existing MARL methods. As examples, we propose neighborhood cognition consistent deep Q-learning and Actor-Critic to facilitate large-scale multi-agent cooperations. Extensive experiments on several challenging tasks (i.e., packet routing, wifi configuration, and Google football player control) justify the superior performance of our methods compared with state-of-the-art MARL approaches.