This work studies the question of Representation Learning in RL: how can we learn a
compact low-dimensional representation such that on top of the representation we can …

We study model-based offline Reinforcement Learning with general function approximation
without a full coverage assumption on the offline data distribution. We present an algorithm …

We present BRIEE, an algorithm for efficient reinforcement learning in Markov Decision
Processes with block-structured dynamics (ie, Block MDPs), where rich observations are …

We study representation learning for Offline Reinforcement Learning (RL), focusing on the
important task of Offline Policy Evaluation (OPE). Recent work shows that, in contrast to …

One of the central challenges in online advertising is attribution, namely, assessing the
contribution of individual advertiser actions including emails, display ads and search ads to …

We consider a contextual bandit problem with $ S $ contexts and $ K $ actions. In each
round $ t= 1, 2,\dots $ the learnerobserves a random context and chooses an action based …

In response to the issue of UAV escape guidance, this study proposed a unified intelligent
control strategy synthesizing optimal guidance and meta deep reinforcement learning …

Off-policy evaluation (OPE) is crucial for assessing a target policy's impact offline before its
deployment. However, achieving accurate OPE in large state spaces remains challenging …

This paper delves into the problem of safe reinforcement learning (RL) in a partially
observable environment with the aim of achieving safe-reachability objectives. In traditional …

Off-policy evaluation (OPE) is one of the most fundamental problems in reinforcement
learning (RL) to estimate the expected long-term payoff of a given target policy with only …