Changepoint-Aware World Models: Detecting Dynamics Shifts and Recovering by Forgetting Stale Replay in Model-Based RL
A robot's learned model of its own dynamics is only valid until those dynamics change: actuators wear, payloads shift, and joints stiffen. A model-based agent that keeps training as if nothing happened adapts slowly, dragged back by a replay buffer full of stale experience. We present Changepoint-Aware World Models (CAWM), a DreamerV3 agent that detects an abrupt dynamics shift from its own internal prediction error, using an online CUSUM test against a rolling baseline that fires only on abrupt change rather than on slow learning drift. It then forgets stale replay, keeping the learned representation while flushing obsolete data. On simulated locomotion under two robot-relevant shifts, d...
| Authors | Everest Yang |
|---|---|
| Primary category | cs.LG |
| Published | 15 July 2026 |
| Last updated at source | 15 July 2026 |
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