93 citations · 122 across the 3 of their papers we have counts for
3 papers
Rotting bandits are not harder than stochastic ones
Julien Seznec, Andrea Locatelli, Alexandra Carpentier +2
In stochastic multi-armed bandits, the reward distribution of each arm is assumed to be stationary. This assumption is often violated in practice (e.g., in recommendation systems),…
Risk-Aversion in Multi-armed Bandits
Amir Sani, Alessandro Lazaric, Rémi Munos
Stochastic multi-armed bandits solve the Exploration-Exploitation dilemma and ultimately maximize the expected reward. Nonetheless, in many practical problems, maximizing the expec…
Transfer from Multiple MDPs
Alessandro Lazaric, Marcello Restelli
Transfer reinforcement learning (RL) methods leverage on the experience collected on a set of source tasks to speed-up RL algorithms. A simple and effective approach is to transfer…