arXiv: cs.AI preprintsInternational6 November 2026
Permanent and Transient Representations for Continual Reinforcement Learning
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arXiv:2611.07327v1 Announce Type: cross Abstract: Continual Reinforcement Learning (CRL) agents struggle to adapt to new situations while retaining prior knowledge, leading to a stability-plasticity trade-off. One approach to solving this problem is by using complementary learning systems: one for slow, long-term learning and another for quick, transient adaptation. Anand & Precup (2023) instantiated this idea using permanent and transient value functions, but used the same features in both systems. Intuitively, long-term learning should rely more on parametric representations for broad genera
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