We consider the task of learning in episodic finite-horizon Markov decision processes with
an unknown transition function, bandit feedback, and adversarial losses. We propose an …

We study federated contextual linear bandits, where $ M $ agents cooperate with each other
to solve a global contextual linear bandit problem with the help of a central server. We …

Motivated by practical needs such as large-scale learning, we study the impact of adaptivity
constraints to linear contextual bandits, a central problem in online learning and decision …

We study reinforcement learning (RL) with linear function approximation under the adaptivity
constraint. We consider two popular limited adaptivity models: the batch learning model and …

We study the problem of Online Convex Optimization (OCO) with memory, which allows loss
functions to depend on past decisions and thus captures temporal effects of learning …

Reinforcement learning (RL) is a very general framework for making sequential decisions
when the underlying system dynamics are a priori unknown. RL algorithms use the …

We consider regret minimization for Adversarial Markov Decision Processes (AMDPs),
where the loss functions are changing over time and adversarially chosen, and the learner …

The framework of online learning with memory naturally captures learning problems with
temporal effects, and was previously studied for the experts setting. In this work we extend …

We consider the non-stochastic version of the (cooperative) multi-player multi-armed bandit
problem. The model assumes no communication and no shared randomness at all between …

This paper considers online switching control with a finite candidate controller pool, an
unknown dynamical system, and unknown cost functions. The candidate controllers can be …