mlsanityData to 7 Oct 2026

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

arXiv 2510.16462 ↗First posted 18 Oct 2025cs.LG stat.MLcited by 0

On the map: Offline RL and imitation learning from data → Imitation learning methods

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.

Key facts. No citing papers in the map yet (data through 7 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.

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10 of 43 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning20 Jul
  2. 2022imitation: Clean Imitation Learning Implementations22 Nov

Other references · 8

  1. 2016Generative Adversarial Imitation Learningcited by 1,576
  2. 2017Learning Robust Rewards with Adversarial Inverse Reinforcement Learningcited by 485
  3. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 388
  4. 2014Robots that can adapt like animalscited by 203
  5. 2023A density estimation perspective on learning from pairwise human preferencescited by 23
  6. 2024Sliding-Window Thompson Sampling for Non-Stationary Settingscited by 15
  7. 2015Review and Perspective for Distance Based Trajectory Clusteringcited by 5
  8. 2024Reinforcement Learning as a Robotics-Inspired Framework for Insect Navigation: From Spatial Representations to Neural Implementationcited by 2

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  1. 2025World Models Unlock Optimal Foraging Strategies in Reinforcement Learning Agents
  2. 2022Combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging task
  3. 2020Evaluation metrics for behaviour modeling
  4. 2019Meta-Learning for Contextual Bandit Exploration
  5. 2025A Smooth Sea Never Made a Skilled SAILOR: Robust Imitation via Learning to Search
  6. 2025Behavioral Exploration: Learning to Explore via In-Context Adaptation
  7. 2024IBCB: Efficient Inverse Batched Contextual Bandit for Behavioral Evolution History
  8. 2023Contextual Bandits and Imitation Learning via Preference-Based Active Queries