DAGR: State-Conditioned Goal Representations via Difference-Aware Goal Cross-Attention
Goal-conditioned reinforcement learning hinges on how the goal is encoded. Contrastive, metric, temporal-distance and information-theoretic encoders disagree on the objective. They agree on one thing. None of them sees the current state, so the embedding cannot mark which part of the goal still needs action, and the policy must recover that cue by inverting both encoders. We propose DAGR, which refines the static embedding of any late-fusion encoder into a state-conditioned one through multi-scale gated cross-attention. A gated residual holds the refinement near the base, and a difference-aware attention rule biases the scores by a per-token state-goal mismatch. A single condition decides...
| Authors | Xing Lei, Wenyan Yang, Xuetao Zhang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 15 July 2026 |
| Last updated at source | 2 October 2026 |
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