# Buzz, Choose, Forget: A Meta-Bandit Framework for Bee-Like Decision Making

arXiv: https://arxiv.org/abs/2510.16462 · HTML: https://mlsanity.com/paper/2510.16462/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 10 of its 43 references that are in the map.
- First posted on arXiv 18 Oct 2025.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)

## Builds on (most cited first, 10 of 10)

- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Learning Robust Rewards with Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1710.11248/) (2017)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [Robots that can adapt like animals](https://mlsanity.com/paper/1407.3501/) (2014)
- [Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning](https://mlsanity.com/paper/2407.15007/) (2024)
- [imitation: Clean Imitation Learning Implementations](https://mlsanity.com/paper/2211.11972/) (2022)
- [A density estimation perspective on learning from pairwise human preferences](https://mlsanity.com/paper/2311.14115/) (2023)
- [Sliding-Window Thompson Sampling for Non-Stationary Settings](https://mlsanity.com/paper/2409.05181/) (2024)
- [Review and Perspective for Distance Based Trajectory Clustering](https://mlsanity.com/paper/1508.04904/) (2015)
- [Reinforcement Learning as a Robotics-Inspired Framework for Insect Navigation: From Spatial Representations to Neural Implementation](https://mlsanity.com/paper/2406.01501/) (2024)

## Abstract (arXiv)

This work introduces MAYA, a sequential imitation learning model based on multi-armed bandits, designed to reproduce and predict individual bees' decisions in contextualized foraging tasks. The model accounts for bees' limited memory through a temporal window τ, whose optimal value is around 7 trials, with a slight dependence on weather conditions. Experimental results on real, simulated, and complementary (mice) datasets show that MAYA (particularly with the Wasserstein distance) outperforms imitation baselines and classical statistical models, while providing interpretability of individual learning strategies and enabling the inference of realistic trajectories for prospective ecological applications.