ZALT learns latent hub states and hub-to-hub dynamics from demonstrations to plan zero-shot solutions for unseen start-goal tasks, achieving 55% success in a 3D maze versus 6% for baselines.
Conservative q-learning for offline reinforcement learning
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Mixed-Density Diffuser achieves new state-of-the-art results on D4RL benchmarks by allowing non-uniform temporal resolution in diffusion planning.
Multitask offline fitted Q-iteration achieves 1/sqrt(nT) generalization rates under shared low-rank structure and reduces complexity for new tasks by reusing the upstream representation.
citing papers explorer
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Zero-shot Imitation Learning by Latent Topology Mapping
ZALT learns latent hub states and hub-to-hub dynamics from demonstrations to plan zero-shot solutions for unseen start-goal tasks, achieving 55% success in a 3D maze versus 6% for baselines.
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Mixed-Density Diffuser: Efficient Planning with Non-Uniform Temporal Resolution
Mixed-Density Diffuser achieves new state-of-the-art results on D4RL benchmarks by allowing non-uniform temporal resolution in diffusion planning.
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Generalisation in Multitask Fitted Q-Iteration and Offline Q-learning
Multitask offline fitted Q-iteration achieves 1/sqrt(nT) generalization rates under shared low-rank structure and reduces complexity for new tasks by reusing the upstream representation.