Title: Architectural Plasticity for Continual Learning
Primary area: reinforcement learning
Keywords: Reinforcement Learning, Plasticity Loss, Continual Learning, Regularization, Optimization

Abstract: Neural networks for continual reinforcement learning (CRL) often suffer from plasticity loss—a progressive decline in their ability to learn new tasks arising from increased churn and Neural Tangent Kernel (NTK) rank collapse. We propose InterpLayers, a drop-in architectural solution that blends a fixed, parameter-free reference pathway with a learnable projection pathway using input-dependent interpolation weights. Without requiring algorithmic adaptation, InterpLayers conserve gradient diversity and constrain output variability by integrating stable and adaptive computations. We provide theoretical guarantees for bounded churn and show that, under mild assumptions, InterpLayers prevent NTK rank collapse through a non-zero rank contribution from the interpolation weights. Across environments with distributional shifts, including permutation, windowing, and expansion, InterpLayer variants (conv-only, full) consistently mitigate performance degradation compared to parameter-matched baselines. Furthermore, lightweight modifications such as dropout improve performance, especially under gradual shifts. These results position InterpLayers as a simple, complementary solution for maintaining plasticity in CRL.