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The paper identifies and formalizes "spectral plasticity loss" in Mixture-of-Experts (MoE) policies within continual reinforcement learning settings, where the ability to learn new skills degrades over time. They derive a tractable proxy for spectral plasticity based on expert feature matrices and introduce SPHERE, a Parseval penalty tailored for MoE policies. Experiments on MetaWorld and HumanoidBench show that SPHERE significantly improves average success under continual RL compared to unregularized MoE baselines, while maintaining higher spectral plasticity.
MoEs, despite their scaling advantages, suffer from a surprising "spectral plasticity loss" in continual RL, but a simple Parseval penalty can recover performance.
In deep reinforcement learning (DRL), an agent is trained from a stream of experience. In a continual learning setting, such agents can suffer from plasticity loss: their ability to learn new skills from new experiences diminishes over training. Recently, Mixture-of-Experts (MoE) networks have been reported to enable scaling laws and facilitate the learning of diverse skills. However, in continual reinforcement learning settings, their performance can degenerate as learning proceeds, indicating a loss of plasticity. To address this, building on Neural Tangent Kernel (NTK) theory, we formalize the plasticity loss in MoE policies as a loss of spectral plasticity. We then derive a tractable proxy for spectral plasticity, one expressible in terms of individual expert feature matrices. Leveraging this proxy, we introduce SPHERE, a practical Parseval penalty tailored for MoE-based policies that alleviates the loss of spectral plasticity. On MetaWorld and HumanoidBench, SPHERE improves average success under continual RL by 133% and 50% over an unregularized MoE baseline, while maintaining higher spectral plasticity throughout training.
