Recent studies in reinforcement learning (RL) have made significant progress by leveraging
function approximation to alleviate the sample complexity hurdle for better performance …

We study the problem of $ K $-armed dueling bandit for both stochastic and adversarial
environments, where the goal of the learner is to aggregate information through relative …

In real-world reinforcement learning (RL) systems, various forms of {\it impaired
observability} can complicate matters. These situations arise when an agent is unable to …

Thompson sampling (TS) is widely used in sequential decision making due to its ease of use
and appealing empirical performance. However, many existing analytical and empirical …

Motivated by applications in large-scale and multi-agent reinforcement learning, we study
the non-asymptotic performance of stochastic approximation (SA) schemes with delayed …

The standard assumption in reinforcement learning (RL) is that agents observe feedback for
their actions immediately. However, in practice feedback is often observed in delay. This …

Bayesian optimization (BO) is a widely-used sequential method for zeroth-order optimization
of complex and expensive-to-compute black-box functions. The existing BO methods …

Abstract We propose Banker Online Mirror Descent (Banker-OMD), a novel framework
generalizing the classical Online Mirror Descent (OMD) technique in the online learning …

This paper studies the problem of stochastic continuum-armed bandit with constraints
(SCBwC), where we optimize a black-box reward function $ f (x) $ subject to a black-box …

This paper offers a nearly optimal algorithm for online linear optimization with delayed
bandit feedback. Online linear optimization with bandit feedback, or nonstochastic linear …