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Attention-Aware Transform Coding is introduced, which allocates bits over a calibration set to minimize attention-aware distortion and achieves near-lossless accuracy at approximately $5.8\times compression.
LOGIC transforms the exploration paradigm: instead of exploring at the action level, it employs an integrated critic to guide a generator in discovering optimal, high-value goals (RTGs), enabling robust exploration beyond the dataset's frontier while avoiding the fragility of complex multi-loss objectives.
Decision Transformers (DTs) have emerged as a powerful paradigm for sequential decision-making in offline learning, yet they face two intrinsic deficiencies: the challenge of specifying optimal Return-to-Go (RTG) targets and a fundamental lack of exploration beyond the offline dataset. While prior studies have attempte...
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