DRATS derives a minimax objective from a feasibility formulation of MTRL to adaptively sample tasks with the largest return gaps, leading to better worst-task performance on MetaWorld benchmarks.
International Conference on Learning Representations (ICLR) , year=
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UNVERDICTED 2representative citing papers
GPLD applies a row-wise Jacobian penalty to DreamerV3's posterior latent distribution, producing higher sample efficiency on DeepMind Control proprioceptive tasks.
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Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling
DRATS derives a minimax objective from a feasibility formulation of MTRL to adaptively sample tasks with the largest return gaps, leading to better worst-task performance on MetaWorld benchmarks.
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Dreaming Smoothly and Sample Efficiently with Gradient Penalized Latent Dynamics
GPLD applies a row-wise Jacobian penalty to DreamerV3's posterior latent distribution, producing higher sample efficiency on DeepMind Control proprioceptive tasks.