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Adaptively Learning to Select-Rank in Online Platforms
We develop a user response model that considers diverse user preferences and the varying effects of item positions, aiming to optimize overall user satisfaction with the ranked list.
RLIF: Interactive Imitation Learning as Reinforcement Learning
We also provide a unified framework to analyze our RL method and DAgger; for which we present the asymptotic analysis of the suboptimal gap for both methods as well as the non-asymptotic sample complexity bound of our method.
Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning
We use a VQ-VAE to learn state-conditioned action quantization, avoiding the exponential blowup that comes with na\"ive discretization of the action space.
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