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This paper proposes SUccessor-to-Novelty (SUN), an indicator derived from successor value functions to identify goals that are both novel and reachable and presents an adaptive goal-selection strategy that leverages these properties, and an accurate yet lightweight pseudocount to avoid the overhead of classic methods.
DAGR transforms static goal representations into dynamic, state-aware embeddings, significantly boosting navigation performance in reinforcement learning tasks.
Leveraging a single demonstration as reusable memory, ReGIL achieves over 75% success in robotic manipulation tasks with less than an hour of training.
