We study a distributed decision-making problem in which multiple agents face the same multi-armed bandit (MAB), and each agent makes sequential choices among arms to maximize its own individual reward. The agents cooperate by sharing their estimates over a fixed communication graph. We consider ...
Specifically, 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 explore-exploit tradeoff. we consider several different communication protocols for sharing information between agents. We develop and ...
Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Mach. Learn. 47(2–3), 235–256 (2002) Article MATH Google Scholar Jiang, D., Ekwedike, E., Liu, H.: Feedback-based tree search for reinforcement learning. In: International Conferenc...
3.1. Multi-agent reinforcement learning problem According to problems P1 and P2, VUEs need to decide which V2I links can be multiplexed and how much power should be allocated to optimize the data delay performance. Hence, each VUE can be regarded as an agent that interacts with the unknown ...
We study the problem of privacy for distributed learning in Multi-Armed bandit (MAB) problem with multiple players. The players must co-ordinate, as choosing the same arm simultaneously results in a reduced reward. We wish to find a policy which maximises social welfare and individual utility, ...
n‐armed bandit problempolicy iteration algorithmreinforcement learningmulti agent machine learning a reinforcement approach multi-agent machine learning: a reinforcement approach by multi agent machine learning a reinforcement approach multi agent machine learning a reinforcement approach user crowd simulation ...
Interface Design Optimization as a Multi-Armed Bandit Problem "Multi-armed bandits" offer a new paradigm for designing user interfaces in collaboration with AI and user data. To help designers understand the potential... D Lomas - Chi Conference 被引量: 5发表: 2016年 A STATISTICAL APPROACH TO...
Then a decentralized multi-agent multi-armed bandit (MAMAB) algorithm is developed for each SBS to decide its own cache strategy based jointly on its past observations and estimated upcoming cache action of other SBSs. This decentralized MAMAB algorithm with $\epsilon $ -calibration enables ...
The trade-off in such a setting can be interpreted as a multi-armed bandit (MAB) problem, which has been extensively stud- ied in Machine Learning literature. For the Angry Birds Competition in 2013, we implemented the "Beau-Rivage agent", a meta-agent that allows us to choose the next...
Collaborative Multi-Agent Multi-Armed Bandit Learning for Small-Cell Cachingdoi:10.1109/TWC.2020.2966599Cong ShenMeixia TaoXianzhe XuIEEE