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Characterizing Replay Retention Under Dynamics Shift in Model-Based Reinforcement Learning

arXiv · AI, language, vision and robotics · article · Sep 16, 2026 · UTC

Adapting to changes in robot dynamics requires learning from new data without discarding experience that may still be useful. In continual model-based reinforcement learning (RL), replay collected before a dynamics change can slow adaptation, while removing it unnecessarily reduces available training data and can be especially costly if earlier dynamics return. We study when recent transitions are preferable to the full replay history. Two quantities characterize this trade-off: change magnitude and age-staleness area under the curve (AUC), measuring how well transition age separates stale fro

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