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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.
