We address the issue of safety in reinforcement learning. We pose the problem in an
episodic framework of a constrained Markov decision process. Existing results have shown …

We study a constrained contextual linear bandit setting, where the goal of the agent is to
produce a sequence of policies, whose expected cumulative reward over the course of …

Safety in reinforcement learning has become increasingly important in recent years. Yet,
existing solutions either fail to strictly avoid choosing unsafe actions, which may lead to …

We study a stochastic bandit problem with a general unknown reward function and a
general unknown constraint function. Both functions can be non-linear (even non-convex) …

We study a repeated persuasion setting between a sender and a receiver, where at each
time t, the sender shares information about a payoff-relevant state with the receiver. The …

A photoplethysmography (PPG) is an uncomplicated and inexpensive optical technique
widely used in the healthcare domain to extract valuable health-related information, eg …

This paper considers stochastic linear bandits with general nonlinear constraints. The
objective is to maximize the expected cumulative reward over horizon $ T $ subject to a set …

The best arm identification problem in the multi-armed bandit setting is an excellent model of
many real-world decision-making problems, yet it fails to capture the fact that in the real …

The safe linear bandit problem is a version of the classical stochastic linear bandit problem
where the learner's actions must satisfy an uncertain constraint at all rounds. Due its …

We address the problem of identifying the optimal policy with a fixed confidence level in a
multi-armed bandit setup, when\emph {the arms are subject to linear constraints}. Unlike the …