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Found 6 papers, 2 papers with code
INSIGHT: End-to-End Neuro-Symbolic Visual Reinforcement Learning with Language Explanations
In this paper, we present a framework that is capable of learning structured states and symbolic policies simultaneously, whose key idea is to overcome the efficiency bottleneck by distilling vision foundation models into a scalable perception module.
Online Policy Learning from Offline Preferences
To address this problem, the present study introduces a framework that consolidates offline preferences and \emph{virtual preferences} for PbRL, which are comparisons between the agent's behaviors and the offline data.
Estimating Treatment Effects Under Heterogeneous Interference
One popular application of this estimation lies in the prediction of the impact of a treatment (e. g., a promotion) on an outcome (e. g., sales) of a particular unit (e. g., an item), known as the individual treatment effect (ITE).
On Modeling Long-Term User Engagement from Stochastic Feedback
An ultimate goal of recommender systems (RS) is to improve user engagement.
Behavior Estimation from Multi-Source Data for Offline Reinforcement Learning
To overcome this drawback, the present study proposes a latent variable model to infer a set of policies from data, which allows an agent to use as behavior policy the policy that best describes a particular trajectory.
Batch Reinforcement Learning from Crowds
This paper addresses the lack of reward in a batch reinforcement learning setting by learning a reward function from preferences.
