Results 81 to 90 of about 4,126,170 (169)
Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits [PDF]
The study of collaborative multi-agent bandits has attracted significant attention recently. In light of this, we initiate the study of a new collaborative setting, consisting of $N$ agents such that each agent is learning one of $M$ stochastic multi ...
Srikant, R. +3 more
core +1 more source
Signal detection models as contextual bandits. [PDF]
R Soc Open Sci, 2023 Sherratt TN, O'Neill E.
europepmc +1 more source
Contextual Restless Multi-Armed Bandits with Application to Demand Response Decision-Making
This paper introduces a novel multi-armed bandits framework, termed Contextual Restless Bandits (CRB), for complex online decision-making. This CRB framework incorporates the core features of contextual bandits and restless bandits, so that it can model ...
Chen, Xin, Hou, I-Hong
core
Adversarial Attacks and Robustness of Combinatorial Multi-Armed Bandits
We study reward poisoning attacks on Combinatorial Multi-armed Bandits (CMAB). We first provide a sufficient and necessary condition for the attackability of CMAB, a notion to capture the vulnerability and robustness of CMAB.
Balasubramanian, Rishab
core +3 more sources
Multi-Armed Bandits in Brain-Computer Interfaces. [PDF]
Front Hum Neurosci, 2022 Heskebeck F +2 more
europepmc +1 more source
Contextual Combinatorial Volatile Multi-armed Bandit with Adaptive Discretization.
, 2020 We consider contextual combinatorial volatile multi-armed bandit (CCV-MAB), in which at each round, the learner observes a set of available base arms and their contexts, and then, selects a super arm that contains K base arms in order to maximize its cumulative reward. Under the semi-bandit feedback setting and assuming that the contexts lie in a space
Andi Nika, Sepehr Elahi, Cem Tekin
openaire +2 more sources
Maximum Entropy Exploration in Contextual Bandits with Neural Networks and Energy Based Models. [PDF]
Entropy (Basel), 2023 Elwood A +3 more
europepmc +1 more source
Multi-Armed Bandits and Quantum Channel Oracles [PDF]
Multi-armed bandits are one of the theoretical pillars of reinforcement learning. Recently, the investigation of quantum algorithms for multi-armed bandit problems was started, and it was found that a quadratic speed-up (in query complexity) is possible ...
Buchholz, Simon +2 more
core +1 more source
VacSIM: Learning effective strategies for COVID-19 vaccine distribution using reinforcement learning. [PDF]
Intell Based Med, 2022 Awasthi R +9 more
europepmc +1 more source
Contextual multi-armed bandits with structured payoffs
, 2020 Multi-Armed Bandit (MAB) problems model sequential decision making under uncertainty. In traditional MAB, the learner selects an arm in each round, and then, observes a random reward from the arm’s unknown reward distribution. In the end, the goal is to maximize the cumulative reward by learning to select optimal arms as much as possible.
openaire +1 more source