# The Hive Mind is a Single Reinforcement Learning Agent

arXiv: https://arxiv.org/abs/2410.17517 · HTML: https://mlsanity.com/paper/2410.17517/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 85 references that are in the map.
- First posted on arXiv 23 Oct 2024.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Mean Field Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1802.05438/) (2018)
- [Streaming Deep Reinforcement Learning Finally Works](https://mlsanity.com/paper/2410.14606/) (2024)
- [Speed-vs-Accuracy Tradeoff in Collective Estimation: An Adaptive Exploration-Exploitation Case](https://mlsanity.com/paper/2201.07123/) (2022)
- [Cross-inhibition leads to group consensus despite the presence of strongly opinionated minorities and asocial behaviour](https://mlsanity.com/paper/2211.09531/) (2022)
- [Congestion and Scalability in Robot Swarms: a Study on Collective Decision Making](https://mlsanity.com/paper/2307.08568/) (2023)

## Built on by (most cited first, 1 of 1)

- [Building Socio-Affective Artificial Intelligence for Interactive Multi-Agent Simulations](https://mlsanity.com/paper/2609.26927/) (2026)

## Abstract (arXiv)

Decision-making is an essential attribute of any intelligent agent or group. Natural systems are known to converge to effective strategies through at least two distinct mechanisms: collective decision-making via imitation of others, and trial-and-error by a single agent. This paper establishes an equivalence between these two paradigms. We show that the emergent distributed cognition (sometimes referred to as the textithive mind) arising from individuals following simple, local imitation-based rules is that of a single online reinforcement learning (RL) agent aggregating action-value samples from many parallel instances of a multi-armed bandit. More specifically, we show that, in the blindly imitative weighted voter model of honey bees' waggle dance, the update rule through which this macro-agent learns is an RL algorithm that we coin textitMaynard-Cross Learning. Our analysis implies that a group of individuals following simple imitative strategies can be equivalent to a more complex learning entity, substantiating the idea that group-level intelligence may explain how seemingly irrational individual behaviors are selected in nature. Beyond biology, the framework offers new tools for analyzing economic and social systems where individuals imitate successful strategies, effectively participating in a collective learning process. Our findings may further inform the design of scalable RL-inspired collective systems in artificial domains.