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HKU and Tsinghua propose VAST: enabling offline reinforcement learning to adaptively select value update timescales

The University of Hong Kong and Tsinghua University propose VAST (Horizon Adaptive Offline Policy Learning via Value Stitching), an offline value learning framework that adaptively selects update horizons by jointly choosing future subgoals and time spans to stitch local returns with subsequent values.

Part of the event: HKU and Tsinghua Propose Offline Value Learning Framework VAST

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