Detecting is Easy, Adapting is Hard: Local Expert Growth for Visual Model-Based Reinforcement Learning under Distribution Shift
Visual model-based reinforcement learning (MBRL) agents can perform well on the training distribution, but often break down once the test environment shifts. In visual MBRL, recognizing that a shift has occurred is often the easier part; the harder part is turning that recognition into useful action-level correction. We study several ways of responding to shift, including planning penalties, direct fine-tuning, global residual correction, and coarse gating. In our experiments, these approaches either do not improve closed-loop control or hurt in-distribution (ID) performance. Based on these negative results, we propose JEPA-Indexed Local Expert Growth. The method uses a frozen JEPA repres...
| Authors | Haiyang Zhao |
|---|---|
| Primary category | cs.LG |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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