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[2602.12753] Hierarchical Successor Representation for Robust Transfer
Summary
The paper introduces the Hierarchical Successor Representation (HSR), addressing limitations of classical successor representation in dynamic environments, enhancing task transfer and exploration efficiency.
Why It Matters
This research is significant as it proposes a novel framework that improves the robustness of predictive representations in machine learning, particularly in environments with changing dynamics. By facilitating better task transfer and exploration, HSR could enhance the performance of AI systems in complex scenarios, making it relevant for advancements in robotics and AI applications.
Key Takeaways
- HSR overcomes classical successor representation limitations by incorporating temporal abstractions.
- It enables stable state feature learning that is robust to changes in task policies.
- The application of non-negative matrix factorization leads to efficient task transfer in complex environments.
- HSR provides a policy-agnostic hierarchical map that bridges model-free and model-based approaches.
- The framework supports efficient exploration in large, procedurally generated environments.
Computer Science > Machine Learning arXiv:2602.12753 (cs) [Submitted on 13 Feb 2026] Title:Hierarchical Successor Representation for Robust Transfer Authors:Changmin Yu, Máté Lengyel View a PDF of the paper titled Hierarchical Successor Representation for Robust Transfer, by Changmin Yu and 1 other authors View PDF HTML (experimental) Abstract:The successor representation (SR) provides a powerful framework for decoupling predictive dynamics from rewards, enabling rapid generalisation across reward configurations. However, the classical SR is limited by its inherent policy dependence: policies change due to ongoing learning, environmental non-stationarities, and changes in task demands, making established predictive representations obsolete. Furthermore, in topologically complex environments, SRs suffer from spectral diffusion, leading to dense and overlapping features that scale poorly. Here we propose the Hierarchical Successor Representation (HSR) for overcoming these limitations. By incorporating temporal abstractions into the construction of predictive representations, HSR learns stable state features which are robust to task-induced policy changes. Applying non-negative matrix factorisation (NMF) to the HSR yields a sparse, low-rank state representation that facilitates highly sample-efficient transfer to novel tasks in multi-compartmental environments. Further analysis reveals that HSR-NMF discovers interpretable topological structures, providing a policy-agnostic hi...