LATIDIA · Robótica

VaDar: Acondicionamiento implícito de objetivos a través de la desagregación de valores

arXiv: 2610.26629v1Tipo de anuncio: Cross Resumen: Las tareas de horizonte largo son importantes para la IA y la robótica modernas, pero plantean un desafío para el aprendizaje por refuerzo. Históricamente, un enfoque popular ha sido descomponer una tas

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arXiv:2610.26629v1 Announce Type: cross Abstract: Long-horizon tasks are important for modern AI and robotics, yet pose a challenge for reinforcement learning. Historically, a popular approach has been to decompose a task into subgoals, and learn a subgoal-conditioned policy to execute each subtask in sequence. We introduce an alternative called Value Disaggregation (VaDar), which trains a single subgoal-independent policy to maximize a linear combination of per-subgoal critic values. Because subgoal information is needed only at training-time, VaDar removes the need for possibly costly or cumbersome subgoal-selection during policy deployment. Moreover, through careful experiments, we show that VaDar (1) is always competitive with, and frequently outperforms, natural baselines like goal-conditioning and (2)