WebJan 22, 2024 · This paper investigates learning-based caching in small-cell networks (SCNs) when user preference is unknown. The goal is to optimize the cache placement in each small base station (SBS) for minimizing the system long-term transmission delay. We model this sequential multi-agent decision making problem in a multi-agent multi-armed … WebSpecifically, we develop and utilize the multi-agent multi-armed bandit (MAB) problem to model and study how multiple interacting agents make decisions that balance the …
Customized Nonlinear Bandits for Online Response Selection …
WebA multi-armed bandit (also known as an N -armed bandit) is defined by a set of random variables X i, k where: 1 ≤ i ≤ N, such that i is the arm of the bandit; and. k the index of the play of arm i; Successive plays X i, 1, X j, 2, X k, 3 … are assumed to be independently distributed, but we do not know the probability distributions of the ... WebJul 10, 2024 · In this paper, we study a distributed stochastic multi-armed bandit problem that can address many real-world problems such as task assignment for multiple crowdsourcing platforms, traffic scheduling in wireless networks with multiple access points and caching at cellular network edge. We propose an efficient algorithm called multi … syred of wade 1st lord of wade
Multi-Agent Multi-Armed Bandit Learning for Online Management …
Webtextual multi-armed bandit model with a nonlinear reward function that uses distributed representation of text for on-line response selection. A bidirectional LSTM is used to pro-duce the distributed representations of dialog context and responses, which serve as the input to a contextual bandit. In learning the bandit, we propose a customized ... http://web.mit.edu/dubeya/www/files/dp_linucb_20.pdf Webthe Pareto frontier of multiple objectives [25] from the perspective of a single agent. We note that other multi-agent variants of the multi-armed bandit problem have been explored … syreeta burney durham nc