Results 31 to 40 of about 3,478,919 (201)

Online Multi-Armed Bandit

CoRR, 2017
We introduce a novel variant of the multi-armed bandit problem, in which bandits are streamed one at a time to the player, and at each point, the player can either choose to pull the current bandit or move on to the next bandit. Once a player has moved on from a bandit, they may never visit it again, which is a crucial difference between our problem ...
Uma Roy, Ashwath Thirmulai, Joe Zurier
openaire   +2 more sources

Indexability and index heuristics for a simple class of inventory routing problems [PDF]

, 2009
We utilise and develop Whittle's restless bandit formulation to analyse a simple class of inventory routing problems with direct deliveries. These routing problems arise from the practice of vendor-managed inventory replenishment and concern the optimal ...
Archibald, T.; id_orcid, Glazebrook, K D, Black, D., Glazebrook, K., Archibald, T W, Black, D   +5 more
core   +5 more sources

Correlation-Aware Collaborative Adaptive Window Algorithm for Multi-Armed Bandits [PDF]

ITM Web of Conferences
The Multi-Armed Bandit (MAB) problem is central to reinforcement learning, where it addresses the trade-off between exploration and exploitation. However, traditional MAB algorithms often encounter difficulties in non-stationary environments with ...
Xu Yaoxin
doaj   +1 more source

Hedging using reinforcement learning: Contextual k-armed bandit versus Q-learning

Journal of Finance and Data Science, 2023
The construction of replication strategies for contingent claims in the presence of risk and market friction is a key problem of financial engineering. In real markets, continuous replication, such as in the model of Black, Scholes and Merton (BSM), is ...
Loris Cannelli, Giuseppe Nuti, Marzio Sala, Oleg Szehr   +3 more
doaj   +1 more source

Efficient crowdsourcing of unknown experts using multi-armed bandits [PDF]

, 2012
We address the expert crowdsourcing problem, in which an employer wishes to assign tasks to a set of available workers with heterogeneous working costs.
Tran-Thanh, Long, Long Tran-thanh, Tran-Thanh, L, Rogers, A, Alex Rogers, Nicholas R. Jennings, Stein, Sebastian, Jennings, Nicholas R., Rogers, Alex, Jennings, NR, Stein, S, Sebastian Stein   +11 more
core   +1 more source

Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits [PDF]

, 2023
In this paper, we study the collaborative learning model, which concerns the tradeoff between parallelism and communication overhead in multi-agent multi-armed bandits. For regret minimization in multi-armed bandits, we present the first set of tradeoffs
Zhang, Qin, Karpov, Nikolai
core   +1 more source

Multi-Armed Bandits and Their Classical Algorithms: Strengths and Limitations [PDF]

ITM Web of Conferences
Multi-armed Bandits (MAB), which is the short form for multi- armed bandit, is playing a significant role in varied spaces including online advertising, recommendation system, clinical trials and medical decision- making and even in 5G. The model aims to
Wu Sichen
doaj   +1 more source

Epsilon–First Policies for Budget–Limited Multi-Armed Bandits

, 2010
We introduce the budget–limited multi–armed bandit (MAB), which captures situations where a learner’s actions are costly and constrained by a fixed budget that is incommensurable with the rewards earned from the bandit machine, and then describe a first ...
Munoz de Cote, Enrique, Tran-Thanh, Long, Jennings, Nicholas R., Chapman, Archie, Rogers, Alex, Munoz De Cote Flores Luna, Jose Enrique   +5 more
core   +2 more sources

Multi-armed bandits in metric spaces [PDF]

Proceedings of the fortieth annual ACM symposium on Theory of computing, 2008
In a multi-armed bandit problem, an online algorithm chooses from a set of strategies in a sequence of trials so as to maximize the total payoff of the chosen strategies. While the performance of bandit algorithms with a small finite strategy set is quite well understood, bandit problems with large strategy sets are still a topic of very active ...
Robert Kleinberg, Aleksandrs Slivkins, Eli Upfal   +2 more
openaire   +3 more sources

Multi-armed Bandits with Compensation

CoRR, 2018
We propose and study the known-compensation multi-arm bandit (KCMAB) problem, where a system controller offers a set of arms to many short-term players for $T$ steps. In each step, one short-term player arrives to the system. Upon arrival, the player aims to select an arm with the current best average reward and receives a stochastic reward associated ...
Siwei Wang 0002, Longbo Huang
openaire   +3 more sources