# The Emergence of Individuality

arXiv: https://arxiv.org/abs/2006.05842 · HTML: https://mlsanity.com/paper/2006.05842/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2020.

## Built on by (most cited first, 22 of 22)

- [Towards a Standardised Performance Evaluation Protocol for Cooperative MARL](https://mlsanity.com/paper/2209.10485/) (2022)
- [Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition](https://mlsanity.com/paper/2211.12712/) (2022)
- [Self-Organized Polynomial-Time Coordination Graphs](https://mlsanity.com/paper/2112.03547/) (2021)
- [System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent Learning](https://mlsanity.com/paper/2305.02128/) (2023)
- [Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2405.15054/) (2024)
- [HyperMARL: Adaptive Hypernetworks for Multi-Agent RL](https://mlsanity.com/paper/2412.04233/) (2024)
- [Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2207.03902/) (2022)
- [Settling Decentralized Multi-Agent Coordinated Exploration by Novelty Sharing](https://mlsanity.com/paper/2402.02097/) (2024)
- [Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2410.08540/) (2024)
- [An Extended Benchmarking of Multi-Agent Reinforcement Learning Algorithms in Complex Fully Cooperative Tasks](https://mlsanity.com/paper/2502.04773/) (2025)
- [The impact of behavioral diversity in multi-agent reinforcement learning](https://mlsanity.com/paper/2412.16244/) (2024)
- [QTypeMix: Enhancing Multi-Agent Cooperative Strategies through Heterogeneous and Homogeneous Value Decomposition](https://mlsanity.com/paper/2408.07098/) (2024)
- [Enhancing Cooperative Multi-Agent Reinforcement Learning with State Modelling and Adversarial Exploration](https://mlsanity.com/paper/2505.05262/) (2025)
- [When Is Diversity Rewarded in Cooperative Multi-Agent Learning?](https://mlsanity.com/paper/2506.09434/) (2025)
- [Open-Ended Diverse Solution Discovery with Regulated Behavior Patterns for Cross-Domain Adaptation](https://mlsanity.com/paper/2209.12029/) (2022)
- [Hierarchical Strategies for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2212.07397/) (2022)
- [Never Explore Repeatedly in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2308.09909/) (2023)
- [Events as Triggers for Behavioral Diversity in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2605.12388/) (2026)
- [CTC: The Composite Task Challenge for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2502.00345/) (2025)
- [Constructive Conflict-Driven Multi-Agent Reinforcement Learning for Strategic Diversity](https://mlsanity.com/paper/2509.14276/) (2025)
- [Heterogeneity in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2512.22941/) (2025)
- [Multi-Agent Reinforcement Learning with Submodular Reward](https://mlsanity.com/paper/2603.06810/) (2026)

## Abstract (arXiv)

Individuality is essential in human society, which induces the division of labor and thus improves the efficiency and productivity. Similarly, it should also be the key to multi-agent cooperation. Inspired by that individuality is of being an individual separate from others, we propose a simple yet efficient method for the emergence of individuality (EOI) in multi-agent reinforcement learning (MARL). EOI learns a probabilistic classifier that predicts a probability distribution over agents given their observation and gives each agent an intrinsic reward of being correctly predicted by the classifier. The intrinsic reward encourages the agents to visit their own familiar observations, and learning the classifier by such observations makes the intrinsic reward signals stronger and the agents more identifiable. To further enhance the intrinsic reward and promote the emergence of individuality, two regularizers are proposed to increase the discriminability of the classifier. We implement EOI on top of popular MARL algorithms. Empirically, we show that EOI significantly outperforms existing methods in a variety of multi-agent cooperative scenarios.