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[2602.19956] Sparse Masked Attention Policies for Reliable Generalization
Summary
This paper presents a novel method for improving policy generalization in reinforcement learning by using a learned masking function integrated with attention weights, demonstrating significant enhancements over traditional approaches.
Why It Matters
The research addresses a critical challenge in reinforcement learning: ensuring that policies generalize effectively to unseen tasks. By introducing a more reliable information removal technique, this work has implications for developing robust AI systems that can adapt to new environments, enhancing their practical applications in various domains.
Key Takeaways
- Introduces a learned masking function to enhance policy generalization.
- Demonstrates significant improvements in task generalization using the Procgen benchmark.
- Addresses the limitations of traditional abstraction methods in reinforcement learning.
Computer Science > Machine Learning arXiv:2602.19956 (cs) [Submitted on 23 Feb 2026] Title:Sparse Masked Attention Policies for Reliable Generalization Authors:Caroline Horsch, Laurens Engwegen, Max Weltevrede, Matthijs T. J. Spaan, Wendelin Böhmer View a PDF of the paper titled Sparse Masked Attention Policies for Reliable Generalization, by Caroline Horsch and 4 other authors View PDF HTML (experimental) Abstract:In reinforcement learning, abstraction methods that remove unnecessary information from the observation are commonly used to learn policies which generalize better to unseen tasks. However, these methods often overlook a crucial weakness: the function which extracts the reduced-information representation has unknown generalization ability in unseen observations. In this paper, we address this problem by presenting an information removal method which more reliably generalizes to new states. We accomplish this by using a learned masking function which operates on, and is integrated with, the attention weights within an attention-based policy network. We demonstrate that our method significantly improves policy generalization to unseen tasks in the Procgen benchmark compared to standard PPO and masking approaches. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2602.19956 [cs.LG] (or arXiv:2602.19956v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.19956 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history...