RS-Diffuser integrates diffusion planners, quantile regression critics, and CVaR-style guidance to produce risk-averse to risk-seeking behaviors from one model in offline RL.
arXiv preprint arXiv:2102.05371 , year=
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FAN simplifies expressive flow policies and distributional critics in offline RL via single-iteration behavior regularization and single-sample noise conditioning to claim SOTA performance with lower training and inference time.
MASK schedules top-K agents via semantic gating and a global encoder to achieve risk-aware multi-robot coordination that matches unconstrained baselines under bandwidth caps.
citing papers explorer
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RS-Diffuser: Risk-Sensitive Diffusion Planning with Distributional Value Guidance
RS-Diffuser integrates diffusion planners, quantile regression critics, and CVaR-style guidance to produce risk-averse to risk-seeking behaviors from one model in offline RL.
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Towards Efficient and Expressive Offline RL via Flow-Anchored Noise-conditioned Q-Learning
FAN simplifies expressive flow policies and distributional critics in offline RL via single-iteration behavior regularization and single-sample noise conditioning to claim SOTA performance with lower training and inference time.
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MASK: Multi-Agent Semantic K-Scheduling for Risk-Sensitive 6G Robotics
MASK schedules top-K agents via semantic gating and a global encoder to achieve risk-aware multi-robot coordination that matches unconstrained baselines under bandwidth caps.