Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning
Continual reinforcement learning must balance retention with adaptation, yet many methods still rely on \emph{single-model preservation}, committing to one evolving policy as the main reusable solution across tasks. Even when a previously successful policy is retained, it may no longer provide a reliable starting point for rapid adaptation after interference, reflecting a form of \emph{loss of plasticity} that single-policy preservation cannot address. Inspired by quality-diversity methods, we introduce \textsc{TeLAPA} (Transfer-Enabled Latent-Aligned Policy Archives), a continual RL framework that organizes behaviorally diverse policy neighborhoods into per-task archives and maintains a ...
| Authors | Lute Lillo, Nick Cheney |
|---|---|
| Primary category | cs.LG |
| Published | 16 April 2026 |
| Last updated at source | 13 October 2026 |
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