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This research introduces "minimum attention," a regularization technique grounded in the least action principle, to enhance reinforcement learning and meta-learning stability. By integrating this method into high-dimensional nonlinear dynamics and modern world models like DreamerV3, the approach achieves faster adaptation with fewer samples and improved energy efficiency compared to standard baselines. The work bridges theoretical control theory with practical deep RL applications, offering a novel pathway for stabilizing policy learning in complex environments.
Meta-reinforcement learning with minimum attention
arXiv:2505.16741v5 Announce Type: replace Abstract: Minimum attention applies the least action principle in changes of control concerning state and time, first proposed by Brockett. The involved regularization is highly relevant in emulating biological control, such as motor learning. We apply minimum attention in reinforcement learning (RL) as part of the rewards and investigate its connection to meta-learning and stabilization. Specifically, model-based meta-learning with minimum attention is explored in high-dimensional nonlinear dynamics. Ensemble-based model learning and gradient-based meta-policy learning are alternately performed. Empirically, minimum attention improves fast adaptation in few shots and reduces variance from perturbations of the model and environment, compared to model-free and model-based RL baseline, and yields consistent gain when integrated into modern world models (DreamerV3, MAMBA). Furthermore, the minimum attention demonstrates an improvement in energy efficiency.
Abstract:Minimum attention applies the least action principle in changes of control concerning state and time, first proposed by Brockett. The involved regularization is highly relevant in emulating biological control, such as motor learning. We apply minimum attention in reinforcement learning (RL) as part of the rewards and investigate its connection to meta-learning and stabilization. Specifically, model-based meta-learning with minimum attention is explored in high-dimensional nonlinear dynamics. Ensemble-based model learning and gradient-based meta-policy learning are alternately performed. Empirically, minimum attention improves fast adaptation in few shots and reduces variance from perturbations of the model and environment, compared to model-free and model-based RL baseline, and yields consistent gain when integrated into modern world models (DreamerV3, MAMBA). Furthermore, the minimum attention demonstrates an improvement in energy efficiency.
Machine Learning (cs.LG); Optimization and Control (math.OC); Machine Learning (stat.ML)
arXiv:2505.16741 [cs.LG]
(or arXiv:2505.16741v5 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2505.16741 Focus to learn more arXiv-issued DOI via DataCite
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