Goal-conditioned RL succeeds over dense rewards because its probabilistic goal-reaching objective aligns naturally with dual control requirements in uncertain, partially observed systems.
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Why Goal-Conditioned Reinforcement Learning Works: Relation to Dual Control
Goal-conditioned RL succeeds over dense rewards because its probabilistic goal-reaching objective aligns naturally with dual control requirements in uncertain, partially observed systems.