ATLAS: Adaptive Topological Learning with Abstract Successors for Continual Learning
Quick summary
arXiv:2608.04334v1 Announce Type: cross Abstract: Contemporary model-free reinforcement learning algorithms can achieve very high performance, but have low sample efficiency and are not robust to changes in the environment. Model-based algorithms have much higher sample efficiency, but still fail when the environment shifts. This paper introduces Adaptive Topological Learning with Abstract Successors (ATLAS) to combat these challenges. ATLAS uses a Grow When Required network with Successor Features in order to achieve high sample efficiency while also robustly tackling catastrophic forgetting.
Key takeaways
- arXiv:2608.04334v1 Announce Type: cross Abstract: Contemporary model-free reinforcement learning algorithms can achieve very high performance, but have low sample efficiency and are not robust to changes in the environment.
- Model-based algorithms have much higher sample efficiency, but still fail when the environment shifts.
- This paper introduces Adaptive Topological Learning with Abstract Successors (ATLAS) to combat these challenges.
Why it matters
“ATLAS: Adaptive Topological Learning with Abstract Successors for Continual Learning” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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