Due to the broad range of applications of reinforcement learning (RL), understanding the
effects of adversarial attacks against RL model is essential for the safe applications of this …

Contextual bandit algorithms are applied in a wide range of domains, from advertising to
recommender systems, from clinical trials to education. In many of these domains, malicious …

Abstract In Internet of Things (IoT) applications, federated learning is commonly used for
distributedly training models in a privacy-preserving manner. Recently, federated learning is …

In offline multi-agent reinforcement learning (MARL), agents estimate policies from a given
dataset. We study reward-poisoning attacks in this setting where an exogenous attacker …

While learning in an unknown Markov Decision Process (MDP), an agent should trade off
exploration to discover new information about the MDP, and exploitation of the current …

In decentralized cooperative multi-armed bandits (MAB), each agent observes a distinct
stream of rewards, and seeks to exchange information with others to select a sequence of …

We study adversarial attacks on linear stochastic bandits: by manipulating the rewards, an
adversary aims to control the behaviour of the bandit algorithm. Perhaps surprisingly, we first …

In real scenarios, state observations that an agent observes may contain measurement
errors or adversarial noises, misleading the agent to take suboptimal actions or even …

Due to the broad range of applications of stochastic multi-armed bandit model,
understanding the effects of adversarial attacks and designing bandit algorithms robust to …

We consider the sequential resource allocation problem under the multi-armed bandit model
in the non-stationary stochastic environment. Motivated by many real applications, where …