Stochastic multi-armed bandits are a sequential-decision-making framewor...
Many real-world machine learning applications are characterized by a hug...
A large variety of real-world Reinforcement Learning (RL) tasks is
chara...
In Reinforcement Learning (RL), an agent acts in an unknown environment ...
Inverse reinforcement learning (IRL) denotes a powerful family of algori...
The most relevant problems in discounted reinforcement learning involve
...
One of the central issues of several machine learning applications on re...
We investigate the problem of bandits with expert advice when the expert...
Uncertainty quantification has been extensively used as a means to achie...
Stochastic Rising Bandits is a setting in which the values of the expect...
Autoregressive processes naturally arise in a large variety of real-worl...
Behavioral Cloning (BC) aims at learning a policy that mimics the behavi...
This paper is in the field of stochastic Multi-Armed Bandits (MABs), i.e...
In reinforcement learning, the performance of learning agents is highly
...
In many real-world sequential decision-making problems, an action does n...
With the continuous growth of the global economy and markets, resource
i...
Warehouse Management Systems have been evolving and improving thanks to ...
Automated Reinforcement Learning (AutoRL) is a relatively new area of
re...
Learning in a lifelong setting, where the dynamics continually evolve, i...
Policy Optimization (PO) is a widely used approach to address continuous...
The choice of the control frequency of a system has a relevant impact on...
Traditional model-based reinforcement learning approaches learn a model ...
We study the problem of identifying the policy space of a learning agent...
Mutual information has been successfully adopted in filter feature-selec...
Policy optimization is an effective reinforcement learning approach to s...
In many real-world problems, there is the possibility to configure, to a...