# Meta-reinforcement learning with minimum attention

arXiv: https://arxiv.org/abs/2505.16741 · HTML: https://mlsanity.com/paper/2505.16741/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 21 of its 29 references that are in the map.
- First posted on arXiv 22 May 2025.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 21 of 21)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023)
- [DeepMind Control Suite](https://mlsanity.com/paper/1801.00690/) (2018)
- [Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning](https://mlsanity.com/paper/1803.11347/) (2018)
- [Model-Ensemble Trust-Region Policy Optimization](https://mlsanity.com/paper/1802.10592/) (2018)
- [Robust Learning with Jacobian Regularization](https://mlsanity.com/paper/1908.02729/) (2019)
- [Model-Based Reinforcement Learning via Meta-Policy Optimization](https://mlsanity.com/paper/1809.05214/) (2018)
- [Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning](https://mlsanity.com/paper/2005.06800/) (2020)
- [Differentiable Particle Filtering via Entropy-Regularized Optimal Transport](https://mlsanity.com/paper/2102.07850/) (2021)
- [The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning](https://mlsanity.com/paper/2008.10857/) (2020)
- [Policy Smoothing for Provably Robust Reinforcement Learning](https://mlsanity.com/paper/2106.11420/) (2021)
- [Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs](https://mlsanity.com/paper/2006.16210/) (2020)
- [Convergence of Policy Gradient for Entropy Regularized MDPs with Neural Network Approximation in the Mean-Field Regime](https://mlsanity.com/paper/2201.07296/) (2022)
- [Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function](https://mlsanity.com/paper/2302.01244/) (2023)
- [Online Meta-Critic Learning for Off-Policy Actor-Critic Methods](https://mlsanity.com/paper/2003.05334/) (2020)
- [MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning](https://mlsanity.com/paper/2403.09859/) (2024)
- [Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization](https://mlsanity.com/paper/2202.01832/) (2022)
- [On the Existence and Computation of Minimum Attention Optimal Control Laws](https://mlsanity.com/paper/1911.10135/) (2019)

## Abstract (arXiv)

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.