While reinforcement learning (RL) achieves tremendous success in sequential decision-
making problems of many domains, it still faces key challenges of data inefficiency and the …

Reinforcement learning is an essential paradigm for solving sequential decision problems
under uncertainty. Despite many remarkable achievements in recent decades, applying …

Behavioral cloning reduces policy learning to supervised learning by training a
discriminative model to predict expert actions given observations. Such discriminative …

We study the problem of identifying the best action in a sequential decision-making setting
when the reward distributions of the arms exhibit a non-trivial dependence structure, which …

In causal bandit problems the action set consists of interventions on variables of a causal
graph. Several researchers have recently studied such bandit problems and pointed out …

We study how to learn optimal interventions sequentially given causal information
represented as a causal graph along with associated conditional distributions. Causal …

Sequential experimental design to discover interventions that achieve a desired outcome is
a key problem in various domains including science, engineering and public policy. When …

Empowered by neural networks, deep reinforcement learning (DRL) achieves tremendous
empirical success. However, DRL requires a large dataset by interacting with the …

Learning good interventions in a causal graph can be modelled as a stochastic multi-armed
bandit problem with side-information. First, we study this problem when interventions are …

This paper studies the problem of designing an optimal sequence of interventions in a
causal graphical model to minimize cumulative regret with respect to the best intervention in …