Sequential decision‐making involves making informed decisions based on continuous
interactions with a complex environment. This process is ubiquitous in various applications …

We study $ K $-armed bandit problems where the reward distributions of the arms are all
supported on the $[0, 1] $ interval. Maillard sampling\cite {maillard13apprentissage}, an …

In this paper we propose a general methodology to derive regret bounds for randomized
multi-armed bandit algorithms. It consists in checking a set of sufficient conditions on the …

This paper introduces a novel backup strategy for Monte-Carlo Tree Search (MCTS)
designed for highly stochastic and partially observable Markov decision processes. We …

We present the first study on provably efficient randomized exploration in cooperative multi-
agent reinforcement learning (MARL). We propose a unified algorithm framework for …

Non-stationary multi-armed bandit (MAB) problems have recently attracted extensive
attention. We focus on the abruptly changing scenario where reward distributions remain …

Many real applications of bandits have sparse non-zero rewards, leading to slow learning
rates. A careful distribution modeling that utilizes problem-specific structures is known as …